Data Science Course in Chennai

FITA Academy will help you learn the skills and techniques needed to work with data, including analysis, modeling, and visualization. This is an excellent option for those looking to gain a strong foundation in data science, and it can be tailored to your specific needs. We provide students with the skills and knowledge they need to succeed in the data science field. The academy offers this Data Science Training in Chennai that is perfect for beginners, intermediates, and experts alike. Students can choose between self-paced online modules or in-person classes that are led by experienced professionals.

Basic Concepts of Data Science

FITA Academy has a well-designed curriculum that helps students learn the basics of data science. We also provide ample opportunity for hands-on learning. In addition, the trainers at FITA Academy are experienced professionals who can help you understand the concepts better.

The goal of this Data Science course in Chennai is to introduce you to the basics of data science by teaching you how to use Python for data analysis and R for statistical modeling. You will learn about different types of data (e.g., text, images, audio), how they are collected, stored, and analyzed. We will also discuss some basic concepts in Module 1 of this data science program, such as classification, clustering, regression, time series forecasting, feature selection, dimensionality reduction, and visualization.

This course is intended for students who have no prior experience in any of these areas but would like to gain an overview of what data science is all about. Here are some basic concepts for you to know what, why and How data science works with its process.

What is Data Science?

Combining statistics, mathematics, computer science, and other related subjects is known as Data Science. In simple terms, it is the process of extracting insights from data using statistical methods. This can be done by creating models or algorithms based on the available data. These models can then be used to predict future trends and outcomes.

Why Do We Need Data Science?

Every business needs to know about data science. Today, businesses gather a lot of information. But they don’t always have the tools to look at all this information. So, they need assistance. This is where the work of data scientists comes in. Through data analytics, they turn raw data into useful information.You will learn everything from scratch from the best Data science course in Chennai to help you get started in this field.Data science typically uses statistics, mathematics, and computer programming to understand data.

Data scientists are needed to then interpret the results of these calculations and applications into a form that is easier for businesses to use. They study information by collecting it, extracting patterns from it and using computational techniques such as machine learning or data mining to make predictions or build models.

How Does Data Science Work?

To understand how data science works, let’s first understand some basic concepts.

• Data: A collection of facts or numbers stored in a database.
• Modeling: Using mathematical formulas to describe real-world phenomena.
• Algorithms: An automated way of solving problems.
• Predictions: Making predictions based on existing data.
• Interpretation: Understanding why something happened.
• Visualization: Presenting data visually.
• Machine Learning: Automating tasks using machine learning techniques.
• Statistics: Describing patterns in data.
• Big Data: Large volumes of data collected over time.
• Data Mining: Finding hidden patterns in data.
• Data Analytics: Combining various tools to extract meaningful insights from data.
• Data Science: Combining data mining, predictive modeling, and visualization to create actionable insights.
• Data Engineering: Creating databases and managing big data.
• Data Science Tools: Software packages designed specifically for data science.
• R Programming Language: Used for data analysis.
• Python: Popular programming language used for data science.
• SAS: Statistical software package used for data analysis.
• Hadoop: Distributed computing framework used for data processing.
• Spark: Scalable distributed computing platform used for data processing.
• Tableau: Data analysis is done with this business intelligence tool.

The above list shows just a few examples of data science. There are many more tools and technologies involved in data science which you will learn in your data science training in Chennai at FITA Academy. Let’s now see how these tools work together to solve complex problems with the process given below.

Data Science Process

Now that we have seen some basic concepts, let’s discuss the process.You will learn this process easily with our Data Science course in Chennai to enable you to get into the field of data science.A number of data visualization tools are now available to help you explore your data and present it. Here’s what happens when you start working with data science:

• Collect data: The first step in data science is collecting data. You must ensure that your data is clean and accurate before starting any analysis.
• Clean up data: Once you have collected the required data, you will need to clean it up. For example, if you are analyzing customer behavior, you might want to remove duplicate records.
• Analyze data: Now that the data is cleaned up, you can start analyzing it. Using descriptive statistics, you may determine which clients are most likely to purchase your products. You may also examine whether there is a correlation between product pricing and sales volume using regression analysis.
• Create Models: After analyzing the data, you can create a model. For example, you can build a decision tree to classify customers into groups.
• Test models: Finally, you can test the accuracy of your model by comparing its predictions against actual outcomes. This helps you identify areas where your model needs improvement.
• Evaluate results: Once you have tested your model, you can evaluate its performance. If the model performs well, you can deploy it in production. Otherwise, you can modify it until it meets your requirements.
• Deploy Model: When you have created an effective model, you can deploy it on a server or cloud service.
• Monitor Results: To monitor the performance of your model, you can collect metrics such as response times and error rates. These metrics help you understand how your model is performing.
• Improve Model: If your model isn’t performing well, you can improve it using machine learning techniques.
• Repeat Steps 1-9: In this way, you can continuously refine your model until it achieves optimal performance.

A complete overview of the various aspects of Data Science will be provided in this Data science Tutorial. You can learn about the Data Science process, using tools like R and Python.

Data Science Components

• Statistics- Statistics is one of the core concepts of data science. It involves finding patterns in large amounts of data.

There are two types of statistics:

• Descriptive
• Inferential

Descriptive statistics describe the characteristics of a population while inferential statistics infer relationships between variables.

Descriptive statistics include mean, median, mode, standard deviation, skewness, kurtosis, etc.

Inferential statistics includes correlation, regression, classification, clustering, principal component analysis (PCA), factor analysis, etc.

• Visualization- Data visualization is another important concept of data science. It allows us to see our data in new ways.

We can visualize data through charts, graphs, maps, tables, etc. There is a plethora of tools available online that gives us the ability to visualize data. Some of them are listed below:

• Tableau Software – A business intelligence tool used to analyze data.
• Microsoft Power BI- An easy-to-use business analytics platform.
• Google Fusion Tables- Allows users to upload their own datasets and visualize them.
• Machine Learning- Machine learning is all about training computers to learn from experience.

It is based on three main components:

• Algorithms- The algorithms we use to train machines.
• Datasets- The data sets we feed into the algorithm.
• Metrics- How we measure the success of the algorithm.

The process of developing a machine learning algorithm starts with defining what problem we want to solve. Then, we define the features of the dataset. We need to select appropriate algorithms and choose suitable metrics.

Once we have defined these things, we can write code to implement the solution.

Machine Learning experienced professionals will guide you through the best data science training in Chennai to help you learn the process and the skills that are required to be a data scientist.

• Deep Learning- Deep learning is a subset of machine learning.

In deep learning, we try to build computer programs that mimic human brain functions.

These programs are called artificial neural networks. They consist of multiple layers of neurons. Every neuron is connected to other neurons and acts as a relay station for passing information between them. Neurons also receive feedback from other neurons. This forms connections between neurons. Artificial neural networks are trained using backpropagation. Backpropagation helps us identify which parts of the network should be changed so that they perform better. The ultimate goal of this Data science course in Chennai is to provide you with the ability to create, interpret, and communicate your own ideas.

Difference Between Data Science with BI (Business Intelligence)

Data science and business intelligence are two fields that are often confused. However, there are some key differences between these two fields. Data science is focused on the statistical analysis of data. Business intelligence is focused on the use of data to make business decisions. Find the below Difference Between Data Science with BI.

Data Science without Business Intelligence

Data scientists work independently. They do not report to anyone else. Their job is to find insights in data.

They may or may not know how to present those findings to others.

Data Science with Business Intelligence

BI teams usually have a dedicated team of data scientists who help companies make sense of their data.

Their role is to understand the business problems and then provide solutions.

They often work closely with IT departments. Get immediate job opportunity after completing our data science course in Chennai as FITA Academy will provide you with 100% placement assistance to find you a perfect job right after completing the course.

Implementing Data Science

As we all know now Data Science is a vast term, and it uses different tools for different processes. Data Science has primarily four main processes and they are, Data Integration and Cleansing, Data Warehousing, Data Analytics, and Data Visualization. Now, let us see the major tools that are used to implement Data Science for these different processes.

Data Acquisition and Cleaning

Data Acquisition is the initial stage of the Data Science lifecycle. There are numerous ways to gather data. But, the real challenge over here is that the collected data should be useful and reliable for the business. Also, the collected data may not always be a structured one. It can be semi-structured or unstructured as well. Further, the collected data will be of voluminous quantity. To ease the workload of the Data Scientists there are some popular ETL tools. Below are the popular ETL Tools and its features.

The Tools used here are Talend, IBM Data Camp, and OnBase

Talend

It was developed in the year 2005, and it is an open-source tool. This tool is designed for deriving at the software solutions for application integration, data integration, and preparation. The major advantages of this tool are that it can be easily managed, scaled, cleaned, designed and collaborated quickly.

Significant Features

• This is an affordable Open-Source tool.
• With Talend, it is easy to develop, deploy, maintain, and automate the tasks.
• This tool has a huge community and a unified platform.
• Talend can not be outdated as soon as it is designed based on present and future requirements.

IBM Data Camp

The prime purpose of this tool is to gather or collect the documents, extract the details or facts, and update the documents into the businesses for further processes. This tool can efficiently perform tasks with more flexibility, accuracy, and rapid automation. This tool is capable of supporting multi-channel capture through processing the documents on different devices like mobile, scanners, fax, and peripherals. Also, this tool makes use of natural language processing and delivers useful information for making a faster decision.

Significant Features

• IBM Data Camp has enriched mobility. It provides improved mobility for iOS and Android apps and also supports SDK features.
• It has the best Data Protection feature. It permits users to access and control the confidential data and also lays restrictions on the content for the users thus, providing the necessary content.
• This tool has the ability to classify the structured and unstructured data quickly even from highly variable and complex documents.

OnBase

It was developed by Hyland. Also, this is a single enterprise of information platform which is primarily designed for processing and managing the user’s content. OnBase focuses on prioritizing the user’s business content to a secured location. Also, this provides relevant information for the users when they require it. This tool permits the organization to be more efficient, capable, and agile by increasing the delivering service quality and productivity and also minimize the risk of the enterprise.

Significant Features

• This is a single platform that supports building content-based applications and supports the various other business systems.
• OnBase could be deployed on the cloud and can be extended in the mobile device and other existing applications that are integrated.
• OnBase is the low-code application platform for development. Besides, it reduces the cost and the time for development as it supports in creating content-enabled solutions quickly.

Amazon Redshift

The Amazon Redshift is the petabyte-scale that is completely managed by the AWS cloud. This warehouse allows the organizations to scale up from a few hundred gigabytes and more. Also, this tool permits users to make use of the data and gather insights for the customers and businesses. The Redshift consists of nodes also known as Amazon Redshift clusters. This provision of clusters permits the users to upload the datasets to a data warehouse. Also, customers can perform the queries and analyses of the data here.

Significant Features

• RedShift could be launched within a VPC and also through the Virtual Networking Environment, where the users have access to the control of the cluster.
• The Data which is stored could be encrypted and installed during creating tables.
• The connection between Redshift and Clients is encrypted using the SSL.
• Also, the number of nodes shall be easily scaled in a few clicks on the Redshift of the Data Warehouse.
• Besides, Amazon Redshift is cost-effective and it does not charge any up-front costs.

SnowFlake

It is the complete relational ANSI SQL warehouse data where the users could leverage the skills and tools of the organization that is already in use. The administration demand for big data platforms and traditional data warehouses is eliminated with the help of snowflakes. The SnowFlake could immediately handle the availability, data protection, optimization, and infrastructure so that the users can give more focus on using the data rather than managing it.

Significant features

• Snowflakes are capable of supporting every form of business data whether it is from machine-generated or traditional sources without any complex procedures in it.
• We can easily scale up and scale down the downtime without any interruption during the storage and computation.
• SnowFlakes has the ability to replicate the data across the cloud providers and also across the cloud regions. It keeps the apps and the data operation without any failures and ensures business continuity.
• We can quickly integrate the snowflake with the package and the custom application tools. Tools such as JavaScript, Node.JS, Spark, R, and Python have the potential to unlock the power of cloud data warehousing for tools and developers to use different frameworks and languages.
• This tool also follows the principle of pay for what we use.

Data Analysis

It is the method of processing, modeling, cleaning, and transforming the data to explore useful insights or patterns for the business in decision-making. The primary operations that are involved in the data analyzing process are extraction, data cleansing, data profiling, and data debug. There are various techniques and methods for data analysis and they are Statistical Analysis, Text Analysis, Inferential Analysis, Descriptive Analysis, Predictive Analysis, Prescriptive Analysis, and Diagnostic Analysis.

Data Analysis Tools: Rapid Miner, Informatica Power Center, and KNIME

Rapid Miner

This tool is primarily created for the researchers and non-programmers who work in the Data Science platform for analyzing the data quickly. This tool efficiently supports importing ML models, and other web applications such as Android, Node JS, iOS, and much more by unifying the complete wheel of Big Data Analytics.

Significant Features

• It provides the platform that provides support for Data processing, building ML models and deployment
• This tool can load data from different frameworks such as Cloud, RDBMS, Hadoop, NoSQL and much more
• Rapid Miner is capable of generating predictive modeling using automated models
• This tool can also support Artificial Intelligence models and Deep Learning models like Gradient Boost, XGBoots, and Random Forests

Informatica Power Center

This is the most widely and commonly used Data Integration tool. Also, according to the recent survey report, it is confirmed that the average revenue of this company is around US Dollar 1.05 billion. It is because this tool provides versatile features and data integration capabilities for its users.

Significant Features

• It helps in extracting the data from different sources and transforming it into the accordance of the business requirements and deploy efficiently into the warehouse.
• This tool proficiently supports grid computing, distributed processing, dynamic partitioning, pushdown optimization, and adaptive load balancing.
• It supports rapid prototyping, validation, and profiling.

KNIME

It makes the Data workflow and its components accessible to all by being open, intuitive, and constantly integrating the new developments.

Significant Features

• It can combine simple text formats like PDF, XLS, JSON, CSV, and XML from the time series data and unstructured data types.
• This tool can connect data warehouses and database for integrating data from Microsoft SQL, Apache Hive, Oracle, and much more.
• KNIME can retrieve and access data from different sources like AWS S3, Azure, Google Sheets, and Twitter.
• This tool can perform all the statistical functions efficiently such as mean, standard deviation, quantiles, and hypothesis testing. Also, this tool can perform dimension reduction, correlation analysis, and workflows.
• KNIME can proficiently filter, sort, aggregate, and join data on the local machines and in the distributed big data environments.

Data Visualization tools

These tools are used for representing the data in a graphical or pictorial format. These tools are created for checking the data analytics visually and to make others understand the complex concepts easily. Usually, the Data Visualization extracts Data from different disciplines like information graphics, scientific visualization, and statistical graphics. These tools help in displaying the information in delightful ways such as pie charts, dials and gauges, geographic maps, infographics, bar diagrams, and ferver charts. The visualization tools are primarily needed in analytics for making data-driven insights and demonstrating the data to other employees easily and quickly in an organization. In short, you can easily give the overview of the data to everyone with this tool.

Data Visualization Tools: Google Fusion Tables, Microsoft Power BI, SAS, and Qlik

Google Fusion Tables

It is the web service that is provided by Google for handling the data. The services are used for visualizing, collecting, and sharing data tables. Also, the Data that is stored in multiple tables can be viewed and downloaded by users. The Google Fusion Tables provides numerous means for visualizing the data with timelines, scatterplots, pie charts, bar charts, and geographical maps to its users.

Significant Features

• Firstly, the Fusion Tables are in the Online Format, and the table always distributes the appropriate version of data.
• It is capable of importing the data by itself and provides visualization instantly.
• It can easily merge with new data upon feeding, and it is always up-to-date.
• Also, this tool always provides what the users need, and it can easily build on the public data set.

Microsoft Power BI

This is one of the analytics services that provide valuable insights to make fast, informed, and accurate decisions. Also, this tool can transfer the data to visuals and enables you to share with others irrespective of any device. Also, this tool is capable of exploring and analyzing data on the Cloud as well. The Power BI shares interactive reports and customized dashboards and supports the organization with built-in security and governance.

Significant Features

• This tool is capable of providing both the self-service needs and the enterprise data analytics needs on a common platform.
• Power BI can share and create interactive data visually over public clouds in the global data center, and therefore complies with the users and regulation needs.
• It simplifies the methods of sharing the massive volume of the data to the users and also analyzes the relevant data.
• Power BI gets support from AI Technology and aids the non-data scientist’s professionals to build ML models easily, prepare data, and find the information rapidly from both the structured and the unstructured data along with images and texts.
• For professionals who are familiar with Office 365 can just connect the data models, reports, and excel queries to the Power BI Dashboards at ease. Also, it helps the professionals to analyze, share, and publish the Excel business data in numerous ways.

SAS

SAS is the most popular statistical software tool that was developed for data management, business intelligence, predictive analysis, and data visualization.

Significant Features

• This tool can reveal the stories that are hidden behind your data. This tool immediately shows the identities and suggestion related methods.
• SAS provides advanced data visualization techniques to guide analysis via auto charting.
• SAS can combine the traditional data sources within the given location for analyzing the geographical context.
• It can join tables and import data for applying essential data quality functions with drag-drop capabilities.

Qlik

It provides a centralized hub that permits every user to share and find the relevant data analyses. Also, this tool is capable of unifying the data from different databases such as Oracle, Cloudera Impala, IBM DB2, Sybase, Teradata, and Microsoft SQL Server. Businesses of different sizes can explore any types of Data such as Simple and Complex on their datasets with the help of data discovery tools.

Significant Features

• It has robust security with centralized sharing features
• It has Hybrid multi-cloud architecture
• The users can create interactive data visualizations for presenting the reports in a storytelling format with just the drop and drag interface.

Data Science Training in Chennai at FITA Academy provides in-depth training of the four major components of Data Science – Data Acquisition, Data Warehousing, Data Cleansing, and Data Visualization clearly under the mentorship of real-time Data Science professionals. Our Trainer provides the complete guidance to have a successful career path in the Data Science domain.

Future Of Data Science

Accurate analysis of data can provide vital insights essential to take major decisions in the businesses. Data Analysis can be integrated with machine learning to render best results with minimum cost to the organization. Data science has made a positive impact in almost every sector, resulting in the phenomenal growth of Data Science in the modern era. Let us see the impact of data science in the arena of automation, IoT, social media and machine learning. Enroll yourself at FITA Academy for the best in class Data Science Course in Chennai to have a blissful future

Data Science Interview Questions and Answers

Finding a data science job can be challenging, whether you’re a recent graduate or a seasoned professional. We’ve compiled a list of common data science interview questions and answers to help you prepare for your next data science interview.

From questions about statistical methods to machine learning, our list covers a range of topics that are essential for any data scientist. If you’re just starting out or want to take your career to the next level with our data science course in Chennai, this list is a great resource for preparing for your next data science interview.

1. Can you explain what Data Science means?

• Data science is the study of gathering, managing, storing, retrieving, processing, analysing, interpreting, presenting, and spreading large amounts of information.
• The study of data is an area that draws from many disciplines that uses knowledge from statistics, computer science, mathematics, engineering, economics, and other fields to make sense of raw data.
• A process where we use different statistical methods like regression, classification, clustering, principal component analysis, etc. to analyze the collected data.
• A set of skills used to solve problems involving extracting meaningful insights from big data sets.
• A mix of many fields, such as statistics, computer science, math, engineering, and others.
• A method of analyzing data using computers and software to discover hidden trends and patterns.

Data science is a rapidly growing field. Companies are increasingly collecting data, and they need skilled workers to help them make sense of it all. In case you are interested in a career in data science, now is the time to get started with our data science course in Chennai in order to get you started on the path to success.

2. Why is data analytics different from data science?

• Data Analytics: It’s a subset of data science which involves applying advanced mathematical techniques on structured or unstructured data in order to gain insight into it.
• Data Science: This is an umbrella term for all the activities involved in collecting, managing, storing, retrieving, processing, analyzing, interpreting, presenting, and disseminating large amounts of data.

3. Can you describe sampling and some of its techniques used?

• In data science, sampling is one of the most important tools. The main reason why we sample our data is because we want to get a representative view of the whole population. We can do this by randomly selecting samples from the entire population.
• There are two types of sampling techniques – simple random sampling and stratified sampling.
  • Simple random sampling is when we select a number of elements at random from the total population.
  • Stratified sampling is when we divide the population into groups based on certain criteria (like gender, age, income level, etc.) and then select a number of elements from each group.

4. Describe the conditions for overfitting and underfitting?

• Overfitting occurs when we fit too much noise into our model. In other words, we try to predict something that doesn’t exist. For example, if we have a model that predicts whether someone will buy a product or not, but the training data only contains people who bought the product before, then the model will overfit and make predictions about people who haven’t even purchased anything.
• Underfitting happens when we don’t include enough features in our model. If we have a model that tries to predict the price of a house, but the training data has no prices, then the model won’t be able to learn any useful information.

5. Difference between the long and wide format data?

There are many ways to format data, but the two most common are the long and wide formats. Each has its own advantages and disadvantages which you will learn in your data science course in Chennai, so it’s important to choose the right one for your needs. Here’s a brief overview of the two formats:

6. What are Eigenvectors & Eigenvalues and define Eigen decomposition?

• Eigenvectors are the basis vectors of a linear transformation. They are also known as eigenvectors.
• An eigenvalue is the value of the characteristic equation of a square matrix. An eigenvector is a vector with non-zero entries whose corresponding eigenvalue is equal to 1.

The eigen decomposition of a square matrix M is defined as follows:

M V D V-1 where V is a matrix containing the eigenvectors and D is a diagonal matrix containing the eigenvalues.

7. Explain how PCA works?

PCA is a dimensionality reduction technique which reduces the dimensions of the dataset without losing any information. It does so by finding the directions along which the variance is maximum. These directions are called principal components.

8. Can you give an example of a case in which p-values are high and low?

A p-value is the probability that you would obtain the same or better results if the null hypothesis was true. If the value is less than 5%, we reject the null hypothesis and conclude that there is evidence against the null hypothesis. On the contrary, if the p-value is greater than 95%, we accept the null hypothesis and conclude there is no evidence against the null hypothesis and say that the data supports the null hypothesis.

The lower the p-value, the stronger our evidence against the null hypothesis, while the higher the p-value, our evidence for the null hypothesis increases.

For example, suppose we want to test whether the average temperature in January is statistically different from the average temperature in July. We calculate a t-test statistic and find that the difference is 2 degrees Celsius. Our p-value is 0.042; therefore, we cannot reject the null hypothesis, and we conclude that the average temperatures in January and July are likely to be similar.

On the contrary, if we wanted to test whether the average height of men is statistically different than the women, we calculated a t-test statistic of 4 inches and found a p-value of 0.01. Therefore, we reject the null hypothesis, concluding that men are taller than women.

When it comes to data science, there is a lot to learn in order to be successful. However, one of the most important things to understand is how to interpret p-values. P-values can be high or low, and it is important to know how to read them in order to make the best decisions for your data. FITA Academy Experts can help you through the data science course in Chennai to learn which p-values are high and low, and what that means for your data.

9. What are the types of Resampling and When it is Done?

Resampling is a method used to improve the quality of predictions and estimate the uncertainty of population parameters such as mean, variance, standard deviation, etc. This process is done to ensure the prediction model is robust and accurate by sampling the data set multiple times and observing how it changes. Resampling helps us understand whether our model is biased towards certain values, and gives us confidence about the accuracy of the model.

There are three types of resampling methods:

• Bootstrap – A bootstrapping technique involves generating samples from a distribution of interest and calculating statistics based on those samples. For example, we could generate 1000 samples from a normal distribution and calculate the mean and standard deviation of each sample. We repeat this process many times to obtain an average value and standard deviation.
• Cross validation – In cross validation, one splits the original data into several parts/folds and trains the model on some of the folds and validates it on the remaining ones. Once the model is trained, it is tested on the entire data set.
• Randomization – This is a simple way to make sure that the model is unbiased. One simply shuffles the data randomly and retrains the model. If the model performs well, then we know there is no bias present in the data.

10. How do you define Imbalanced Data?

Data is said to be highly skewed if it is distributed unevenly across different categories. For example, if there are 10 times more images of cats than dogs, then the data set is considered to be imbalanced. This imbalance creates problems for machine learning algorithms because they require balanced data sets to perform well. If the dataset contains too many examples of one class, then the algorithm will learn to predict that class. However, if the dataset contains too few examples of another class, then the algorithm won’t know how to classify those examples correctly.

The problem becomes even worse when we look at image classification tasks like facial recognition and object detection. In such cases, the number of positive samples (images containing faces or objects) is much smaller compared to the negative samples (images without faces or objects). As a result, most models tend to overfit the training data and fail to generalize to unseen test data.

To help you learn about this, FITA Academy’s Data science training in Chennai will guide you with a few ways to deal with imbalanced data, but the most common is to oversample the minority class or undersample the majority class.

11. How does the expected value differ from the mean value?

The difference between the expected value and the mean value is subtle. You might think that the expected value is always greater than the mean value because you know that the average number of people who attend a party is less than the total number of guests. However, this isn’t true. For example, the mean value for the number of people attending a party is 5 and the expected value is 4.5. This is because some parties have fewer than five people while others have more than 10. In fact, most parties have an expected value of around four. If we add up the numbers of people who attended each party, we find that the sum is equal to the total number of people invited. Therefore, the mean value is actually closer to the actual number of attendees.

In statistics, the expected value is often written as E(X), where X represents a random variable. The mean value is usually written as μ, although sometimes it is written as M.

The expert will guide you with each and every step with conceptual examples in your data science course in Chennai which is perfect for beginners in data science to understand the concepts easily.

12. How would you define Survivorship Bias?

Survivorship bias is a common cognitive bias where people tend to focus on facts that support their beliefs and ignore evidence to the contrary. In statistics, it refers to the tendency to look for patterns among events that happened in the past rather than looking at what might happen in the future. For example, if someone believes that humans are responsible for global warming, he or she might overlook evidence that suggests otherwise.

The term was coined by psychologist Daniel Kahneman in his book Thinking Fast and Slow. He used the term to describe the way people think about the world around them. They see things happening now and assume that they will continue to happen in the future. However, they don’t consider the possibility that something could change and make the current situation obsolete.

13. What is KPI?

KPI stands for Key Performance Indicator. It is a metric that can help organizations measure performance. KPIs are commonly used in business management and finance. A good KPI should meet three criteria:

• Be easy to understand
• Have clear goals
• Provide actionable information

14. What is Lift?

Lift is a measurement of how well a model performs on new data. When we say “lift”, we are referring to the increase in accuracy of our predictions. For example, When we use a logistic regression model, with two features, then the lift is 2.0. This means that adding one feature increases the prediction accuracy by 2%.

15. What is model fitting?

Model fitting is the process of finding the best set of parameters (or weights) for a given model. Model fitting involves using statistical methods such as linear regression, logistic regression, decision trees, neural networks, etc. With hands-on practical exercises, you will have a good understanding of how to use these statistical methods in an R program which is covered in your data science training in Chennai.

16. What is Robustness?

Robustness is the ability of a model to perform well under different conditions. For example, a model that predicts whether a person will buy a product based on their gender and age will be robust against changes in these variables. On the other hand, a model that uses only one variable like income may not work when there are multiple factors influencing purchase decisions.

17. What is DOE?

DOE stands for Design of Experiments. It is a method for evaluating models. We can use DOE to test various combinations of input values and observe which combination gives us the highest accuracy.

18. What are confounding variables?

Confounding variables are variables that affect both dependent and independent variables. Confounding variables can lead to biased results. For example, if an experiment shows that increasing the price of a product leads to higher sales, this does not necessarily mean that increasing prices always leads to increased sales. If the price of the product decreases, then sales will decrease too. So, the effect of price on sales depends on many other factors besides just the price itself.

19. What is Selection Bias?

Selection bias occurs when researchers choose participants who have characteristics similar to those of the population from which they were drawn. The result is that the sample is not representative of the population.

For example, if you want to study the effects of smoking on health, you would probably exclude smokers from your sample. The reason is that smokers have more health problems than non-smokers. As a result, the findings of your research cannot be generalized to all smokers.

20. Explain the types of Selection Bias?

There are many different types of selection bias, and FITA Academy trainers can help you navigate through them to ensure you’re making the best choices in your data science course in Chennai to gain the best career opportunities in the data science industry. Here are some types of Selection Bias listed below:

• Sampling Bias – where the researcher chooses participants who have characteristics similar or identical to those of the population.
• Self Selection Bias – where the researcher selects participants who share his/her own interests.
• Volunteer Bias – where the researcher recruits people who are interested in participating in the study.
• Response Bias – where the researcher asks questions that are likely to elicit certain responses.

21. Explain bias-variance trade-off?

Bias-variance trade-offs occur when we try to find the optimal balance between bias and variance. Variance refers to the amount of error associated with each observation. In general, the smaller the variance, the better the estimate. However, the larger the variance, the less precise the estimate. To minimize the risk of overfitting, we need to reduce our variance by reducing the number of observations used to fit the model. This means that we must increase the size of our training data.

22. How do we deal with Bias-Variance Trade-Offs?

We can use cross validation techniques to address this problem. Cross validation helps us determine how much data should be used to train the model.

23. Explain why we need to use Cross Validation Techniques?

Cross validation techniques help us avoid overfitting. Overfitting happens when we build a model using too few samples. This causes the model to fit the noise instead of the signal. When we apply the model to new data, it will give us inaccurate predictions.

24. Why do we need to use Cross Validations?

Cross validations allow us to evaluate the performance of a model without making any assumptions about the underlying distribution of the data.

25. What is K-fold Cross Validation?

K-fold cross validation is a technique that divides the dataset into k equal sized subsets. Then, we randomly assign k partitions to the learning algorithm. Next, we run the learning algorithm on each partition. Finally, we average the results across all the partitions. We repeat this process until every subset has been assigned to the learning algorithm.

26. What is Leave One Out Cross Validation?

Leave one out cross validation (LOOCV) is a special case of k-fold cross validation. It involves leaving out one observation at a time and running the learning algorithm on the remaining observations.

27. What is Bootstrap Cross Validation?

Bootstrap cross validation is another name for leave one out cross validation. It uses random resampling to generate multiple datasets. Each dataset contains n – 1 observations. We then run the learning algorithm on these datasets. Finally, we average all the results together.

28. What is a confusion matrix?

The confusion matrix is a table indicating the performance of a binary classifier on a given dataset. A confusion matrix has 2 rows and 2 columns. Each row represents one class, while each column represents another class. In other words, for a binary classification problem, there are four possible outcomes: positive, negative, true positives, and false negatives.

As a result, if this isn’t the case learn more about confusion matrix through the FITA Academy’s data science course in Chennai and figure out what you need to do to create one with your own data and model, in addition to the best way to go about it.

29. What is logistic regression and explain how it works?

Logistic Regression is also called the logit model. It is a technique to make predictions about the probability of an event occurring. In our case, it predicts whether a candidate will win or lose an election. We use the term “logistic” because we are predicting a binary outcome—either the candidate wins or loses.

For example, suppose we wanted to know what the probability is that Donald Trump will win the presidency. This is a very simple problem. If we take the total number of votes cast for him, we divide it by the number of votes he needs to win. If he gets 50% plus one vote, then he will win; otherwise, he won’t.

This is exactly what logistic regression does. Let’s say we had some data points describing how many people voted for each candidate in the 2016 presidential election. Then, we could fit a logistic regression model to predict the probability that Trump would win based on those numbers.

Let’s look at an example. Suppose there were 10,000 voters in the United States. Of those, 3,500 voted for Hillary Clinton and 7,500 voted for Trump. Now, we want to calculate the probability that Trump will win. To do this, we must solve 2 problems. First, To begin with, we must determine how many votes Trump needs to win. Second, we need to determine the probability of winning given that many votes.

To answer the first question, we simply add up the number of votes for Trump and subtract the number of votes for Clinton. In this case, that gives us 4,900 votes. But, since we don’t know how many votes Trump actually received, we need to estimate his true vote count.

We should assume that Trump’s votes are spread out like a normal distribution. An example of a normal distribution is the following, which is symmetric around the mean:

As you can see, it peaks at zero, meaning that most of the votes for Trump fall within a range centered around 0.Therefore, we can assume that half of the votes for Trump were cast fell within the range -2,700 to +3,300, and half fell outside that range. Therefore, we can set our predicted value to be halfway between the maximum and minimum values.

30. Describe the concept and working of a random forest?

The Random Forest Classifier is one of the most widely used supervised machine learning techniques. There are many decision trees in a random forest. A bootstrap sample is a randomly selected subset of samples used to train each tree. We use the term “random” here because each tree is built independently of others. This independence leads to different decisions being taken by different trees. In fact, each tree makes a prediction based on its training data and then this prediction is compared against the actual label assigned to the test instance. If the predicted value is closer to the true label, then it is considered correct. Otherwise, it is wrong.

This process continues till every single node in the decision tree becomes a leaf node. At that point, the tree stops growing further. Finally, we take the average of predictions made by all the trees in the forest.

Random forests are extremely useful for dealing with high dimensional datasets where there are too many features to consider individually. For example, It’s impossible to evaluate all combinations of feature values if you have a dataset with hundreds of thousands of features. However, a random forest allows us to do this efficiently.

31. How does deep learning differ from machine learning and what are the differences between them?

Among the most popular topics in Artificial Intelligence (AI) is deep learning. This topic deals with the application of AI techniques to solve real world problems.Deep learning algorithms include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), etc. These models are used to classify images, detect objects, play video games, translate languages, etc.

Machine Learning is another important branch of AI. Machine learning uses statistical methods to learn patterns from training examples without being explicitly programmed. For example, we can use a computer program to teach itself how to recognize cats just by feeding it enough pictures of cats. Machine learning is often applied to natural language processing, speech recognition, robotics, recommendation systems, game playing programs, image classification, spam filtering, etc.

In contrast to traditional machine learning, deep learning aims to mimic the biological processes of the human brain. And deep learning algorithms mimic the way neurons communicate with each other inside our brains. We call this process unsupervised learning since there are no instructions telling what to do.

32. How do gradients and gradient descents work?

A gradient is a mathematical representation of the steepness of a function. A gradient represents the direction in which the function changes most rapidly. This helps us understand whether the function increases or decreases as we move along the direction of the gradient.

Gradient descent is a method used to find the global minimum of a function. Starting with some initial guesses about the solution, we then iteratively update our guesses based on the difference between current and previous values.

The process continues till the difference becomes zero.

33. What are the feature vectors?

A feature vector is an n dimensional vector of numerical features that represents an object. For example, you could use a feature vector to describe an animal like a cat, such as “has four legs”, “is black and white”, “lives in the wild”, etc. Feature vectors can be used to classify objects into groups based on similar properties.

In machine learning, feature vectors can be used to represent numeric or symbolical characteristics (called features) that make up an object. For example: a feature vector could be used to describe a person as having blue eyes, brown hair, a large nose, etc. Feature vectors are most commonly used to categorize data into different classes. One common application is to label images as being either cats or dogs. Another example might be assigning each customer in a database to one of three categories depending on their purchase history.

34. Describe the steps involved in making a decision tree?

Decision trees are used to classify data based on some attribute. They work like this: You take the whole data set as input. Then you look for a split that maximises the separation of the classes (i.e., the difference between the two groups). This split is called a node. At each node, you apply the same process again to the divided data. If you stop at a particular node, you know that there is no further splitting possible. Each node represents a class. For instance, let’s say we want to categorize customers based on gender. We could use a decision tree algorithm to find out whether male or female customers spend more money on our products.

The first thing we do is take the entire data set as an input. We divide it into two parts: males and females. Next, we look for a split that separates the two groups. In this case, the best split is gender Male versus gender Female. Because the split is good, we continue applying the same process to both halves.

For example, if we had a customer database of 1000 records, we might start with a split by age group. The next step would be to split by income level. And so on. Once we have found all the splits, we end up with two sub-groups.

Here are the steps involved in making a decision tree:

• Selecting the right variables for splitting nodes – The first step involves deciding which attributes should be used to split a node. Each attribute has a cost associated with it. Attributes with higher costs are more useful for splitting than those with lower costs.
• Choosing the best split criterion – After selecting the attributes, the next step is choosing the best criterion to determine if a given node should be split or not. There are many criteria available including Gini index, information gain, entropy, etc.
• Finding out if the node should be split – Once the best criterion is chosen, the final step is finding out if the node needs to be divided or not. If the node does need to be split then the next step is determining where the cut should occur.
• Calculating the cost of every possible split – Once all these steps are done, you’ll be able to figure out how much each split will cost. These costs are added together to get the overall cost of the entire tree. These costs are added together to get the overall cost of the entire tree.
• Growing the tree – Finally, once the optimal tree has been found, it is grown by adding new nodes to the tree until the desired number of trees is reached.
• Pruning the tree – Once the tree is complete, pruning is performed to remove any unnecessary branches from the tree.

Making a decision tree can be a daunting task, but with the right steps, it can be a breeze. FITA Academy’s data science course in Chennai will train you with the steps involved in making a decision tree, so you can be confident in your decisions.

35. What does root cause analysis mean?

Root cause analysis was originally developed in the 1960s to help prevent industrial accidents. This approach involves identifying the factors that led to the accident and analyzing how each contributed to it. If you find out what caused the problem, you can avoid recurrence of the same issue.

The term “root cause analysis” is often confused with “cause and effect analysis.” Cause and effect analysis looks at the sequence of events leading up to a problem; whereas, root cause analysis looks at all contributing factors that lead to the problem.

For instance, if you don’t know why your car won’t start, you might check how much petrol is in the tank, whether the battery is dead, whether the ignition switch is faulty, etc. You could even take apart the vehicle to see whether there is something wrong with the wiring or fuel pump. However, if you discover that the spark plugs are dirty or the battery cable is loose, those are both fixable issues that would prevent the car from starting again. Those things aren’t considered part of the chain of events that leads to the problem because they don’t contribute directly to the problem itself. They’re just symptoms of the real problem.

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