Data Science Course In Chennai

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Learn Data Science Course in Chennai at FITA - Rated as No 1 Data Science Training institute in Chennai by leading Data Scientists from the industry.

The Data Science Course at FITA is the right track for any aspirant to become an expert in the field of Data Science. Designed by our faculty data scientists with immense experience in the industry, the course combines sound theory coupled with real-time projects in the subject.

Course Highlights & Why Data Science Course at FITA?


Course taught by industry experts with ample experience in the field
You can join the course at any of our four branches in Chennai or our branch at Coimbatore & Madurai.
Latest equipment and software versions.
Small batches (5-6-students only per batch) that ensure individual attention.
Training expertise vouched by numbers: we have trained over 15,000+ aspirants to become IT professionals.
Real-life projects and case studies.
Unlimited Lab-time and Usage.
Regular contact-sessions with visiting industry experts.
With established contacts with over 600+ corporations.
Placement Cell that constantly strives to provide 100% placement assistance.
Course timings designed to suit working professionals and students.
Interview tips and training.

Data Science Course At FITA Consists Of The Following Tracks:

Data Science Course using Python
Data Science Course using R
Data Science Course using SAS
Data Visualization using Tableau

Students can opt for either Data science course using Python or Data Science course using R. If students are interested in mastering both R and Python, they can go for an integrated Data science master's programme in which they will learn R, Python, Machine Learning, SAS and Tableau.

R and Python Programming courses will help the student to learn and apply data analysis. We give a firm foundation in Statistical analysis and methods for the purpose of further analysis and interpretation. If you need to visualize your data through simple dashboards and worksheets, you can also learn Tableau training in Chennai at FITA.

Equipped with the latest infrastructure and software versions, we are fully geared to take students to the next level in data science. Moreover, we work with more than 600+ corporate companies including multinationals. Students will have access to datasets from the real time scenario to practise during training sessions.

Along with the help of our placement assistance cell, we assure a smooth transition for the aspirant from an amateur to a seasoned professional in Data Science.

FITA provides the best-in-class Data Science Certification, with the right proportion of practical and theoretical training, enabling students to acquire holistic knowledge in Data Science. Training is provided by professionals who have a decade of experience in Data Science, which helps students to learn up-to-date industrial practices in Data Science. The syllabus of our course is designed by industrial experts in such a way to bridge the gap between theoretical knowledge and actual industrial requirements.

Undertaking a data science course helps aspiring candidates to equip themselves with the skills required in the industry. Students can try and implement the techniques and tools learned in classrooms with guidance from the trainers. This enables the students to learn the nuances in working on the tools and techniques, which helps them while working on real-time projects in their career.  A low student–trainer ratio, with 5-6 students per class, provides more focus on individual students and encourages the students to interact with the trainers for a clear understanding of the concepts taught. Data Science Machine Learning has benefitted a large number of students in their dream jobs. With continued guidance even after course completion and exceptional placement support, FITA provided the best in class training to our students covering the vast syllabus in Data Science

FITA aims to nurture the aspirants with a whole range of skills required to become a Data Scientist. Students are trained using real-time projects in Data Science, which provides them much clarity on their job as a Data Scientist. Our low student-trainer ratio enables classroom teaching to be an interactive learning experience and to focus on each student's learning. Our Data Science certification has been acknowledged widely by top MNC’s, thus leading one towards their dream career as a Data Scientist. We provide continued guidance even after course completion along with 100% placement support, thus converting today’s aspirants to tomorrow’s Data Scientists.

What Is Data Science?

Data Science is data-driven science that strives to give meaning through analysing and interpreting large amounts of complex data. This data is usually generated in the form of raw information that streams in from various sources and stored in enterprise-level data warehouses.

Data science blends inferences from the stored data, development of suitable algorithms and further the technology to solve complex problems.

Diving deep into storehouses of data helps a business to understand complex behaviours and trends. By helping to bring out hidden insights, the business will be able to make smarter business decisions quickly.

Data has become an important driver for a variety of businesses in recent times. The growth of Communication Technology and Information Technology resulted in the generation of voluminous data and the emergence of new techniques and tools to analyze data; giving birth to a new field of study known as Data Science. Data Science cuts across various sectors creating positive impacts on the development of businesses, thus creating huge demand for Data Scientists worldwide. Business insights analyzed from data has placed many organizations in the right path of development, thus urging many organizations to incorporate Data science in their businesses and hire skilled Data scientists. Recent trends in job markets depict the rising demand for people equipped with Data Science skills and organizations are willing to hire Data Scientists for a higher pay scale.

Data Science course in OMR at FITA helps to build your skills in the tools and techniques involved in Data Science to make a successful career.

Opportunities For Data Science Technology In The Market

Fuelled by requirements for applications that incorporate big data and artificial intelligence, demand for data science is consistently growing. P&G uses data science generated time series models to understand the future demands of their products and Netflix uses data science to understand movie viewing patterns of their audience to decide which series they should produce next.

However, the supply is not growing at the pace of demand. Therefore, it is the best time to become a data scientist. More employers are looking to employ data scientists. Major corporations require data scientists to turn the large amounts of data streaming in through social media and e-commerce sites into action. Most companies also view data scientists as the right path to embracing AI technologies.

Becoming a data scientist is not so difficult as questioned by many students. Candidates who have skills in working on the tools and techniques of data science are vital to become a data scientist. Equipping yourself with the technical skills along with statistics and applied mathematics helps you to prosper in career as a  data scientist.

A person should have hands-on experience in the tools and programming languages like R or Python which are widely used by data scientists. An aspiring data scientist would have thorough practical knowledge about the functionality of the tools and methods used. In recent days, numerous online platform offers data science courses, but could not convert learners to data scientists due to lack of continued guidance and personal training.

Data Science course in Chennai, provided by FITA, covers a wide syllabus which helps to land in your dream career as a Data Scientist. Training is provided by professionals with more than a decade of experience in this field and with exceptional placement support making FITA the best Data Science Training institute in Chennai.

Competency is a keyword to be kept in mind if you wish to be hired as a data scientist. With the increasing demand for data scientists, companies are in search of candidates with exceptional skills in data science.

A data scientist should have sound analytical skills, technical skills to perform tasks using various tools and techniques, programming ability, knowledge in statistics and understanding of the business.Many aspiring data scientists, fail to understand the requirements of the industry due to the numerous guidance they receive from various sources, which provides superficial knowledge about Data Science.

In short, Data Scientist is a person, who finds the important aspects of data using math and statistics skills, correlates and finds the linkage between different sets of data, develop models with the data using programming languages like Python or R and provide valuable business insights or strategies for the company. Possessing exceptional knowledge in statistics without sufficient programming skills or a clear understanding of the business leads nowhere close to becoming a data scientist. One must possess hands-on experience in the tools used in the field of Data Science. Arriving at vital findings from data for developing business strategies using the data science tools and technique makes an authentic data scientist.

Though most of the companies hire freshers from IITs, aspiring candidates from any university with expertise in skill sets can become a data scientist.Data Science certification course in Chennai, provided by FITA, helps you to acquire the desired skill sets to land in your dream career as a Data Scientist. Data Science Training in Chennai at FITA is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their career as a data scientist.

Data Science, as a field, has grown rapidly in recent years and the demand for quality Data Scientists are high. Below are some common skills, which will be expected of an aspiring Data Scientist by various companies.

Programming language- A candidate should be well versed in coding using programming languages like Python, R and querying language like SQL. Python & R are used by a vast majority of organizations and they would like to hire a candidate with an excellent skill set in these programming languages.

Data Visualisation- Data scientists should visualize the data using the visualization tools like Matplotlib, Tableau and various other methods, to convert the results into an understandable format. These tools display the results in the form of graphs, bar-charts, pie-charts, etc. Having hands-on experience in these tools, helps the organization to derive business insights quickly from the data processed. Thus a data scientist is expected to possess these skills.

Machine Learning - A person is expected to know Machine Learning methods, if the company’s product itself if highly data-driven (e.g, Google, Facebook, Uber, etc.). Candidate should have a clear understanding of the applicability of the following ML methods like K-Nearest Neighbour, ensemble methods, random forests, support vector machines, etc. to deduce the most vital insights from the processed data.

Statistics- Statistics is vital for a data scientist to understand various techniques which have a valid approach. candidate should be well-known with statistical tests, distributions, etc. A deep understanding of statistics helps the data scientist to provide valuable insights to make strategic business decisions.

Communication skills - Organisations that hire Data Scientists, expect the candidate to have sound communication skills, so that, the technical findings of a data scientist will be known within the organization across non-technical departments (sales, marketing, etc.). The clarity in communication saves a lot of time and resources, thereby increasing business productivity.

Anyone willing to become a data scientist can acquire and develop their skills by joining the Data Science course in Chennai, provided by FITA. Training is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their career as a data scientist. 

Anyone willing to become a data scientist can acquire and develop their skills by joining the Data Science course in Chennai, provided by FITA. Training is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their career as a data scientist. Aspirants residing nearby Tambaram can enroll yourself in Data Science Training in Tambaram at FITA.

Data science has become the most prominent word in recruitment sites due to its demand in various organizations around the world. You could have noticed various designations like Data Scientists, Data Analyst, Data Engineer, and various other terms also. Some people tend to think that these terms are synonymous and use them interchangeably. Although, all the three roles involve the usage of data, let us discuss the differences among Data Scientist, Data Analyst and Data Engineer.

The key difference lies in the various tasks they perform using the data. 

Data Analyst: Data Analysts add value to the organization by utilizing the data to answer questions and arrive at better solutions for business problems. This is the role predominantly given to entry-level-professionals in the Data Science field. The common tasks of a Data Analyst comprise of data cleaning, creating visualizations of the findings thereby helping the company to make better data-driven decisions.

Data Scientist:  Data Scientists use their expertise in statistics and develop Machine Learning models to make predictive analysis and answer vital business problems. Data scientists unfold business insights from the data using supervised or unsupervised learning methods in their ML models. Data scientists train their mathematical models for better identification of patterns to predict the trends of business accurately. The key difference between a Data Analyst and Data Scientist is that Data scientist provides a whole new approach of understanding data and builds models for new questions whereas a Data Analyst analyses recent trends using the data and convert the results for key business decisions.

Data Engineer: Data Engineers help in optimization of the systems, allowing data scientists and analysts to perform their task. The task of a data engineer is to make sure data is properly collected, stored and made available to its users. Data engineers should possess strong technical knowledge for the creation and integration of API (Application Program Interface) and helps in the maintenance of the data infrastructure. 

In the following table, you can find the skill set required for these three roles in Data Science.

 
Data EngineerData AnalystData Scientist
SQL AnalyticsR, Python coding
Data warehousingData warehousingSQL
HadoopSQLML algorithms
Data ArchitectureStatistical skillsData Mining
Data Visualisation & reportingData Visualisation & reporting

Data optimisation and decision making skills

 

Data Science has grown rapidly in recent years due to its wide applicability in various sectors and helps in strategic decision making for organizations.

Anyone can achieve great heights in Data Science with the appropriate skillset, and if you wish to acquire skills in Data Science, you can enrol in the Data Science course in Chennai, provided by FITA. Training is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their careers as data scientists.

There are ample job opportunities for our students on course completion. Students are trained in higher-level languages like R, Python, and SQL, by professional trainers with hands-on experience in the field. With the skills acquired here, you can land in your dream job in Data Science. Below we have listed a few of the roles which are in huge demand. Business Intelligence (BI) Developer Business Analyst Data Architect Applications Architect Machine Learning Scientist Machine Learning Engineer Statistician

Submit the quick enquiry form for more details to learn the Data Science Training in Chennai at FITA.

The hiring process for the role of data scientist differs based on companies. 

Most of the startups will have an aptitude test comprising probability, statistics, logical reasoning, etc. Programming tests will be conducted to check your skills in Python, R or SQL. On clearing the test, there will be a final interview by the HR or Technical team.

In MNCs, there will be an aptitude test as the first round, followed by an interview with a senior data scientist or person in any designation equivalent to it. Here the technical knowledge of the candidate is gauged and if the candidate is technically eligible, there might be a technical test to check the ability and expertise of the candidate in advanced tools utilized by a data scientist. In some companies, the candidate's way of thinking and problem-solving approaches are also evaluated before hiring.

To improve yourself with advanced tools like Python and R, join the Data Science course in Chennai, provided by FITA. FITA helps aspiring candidates to land in their dream job as a data scientist and excel in it by strengthening the fundamentals during the course. Candidates residing in and around velachery can join Data science training in velachery at FITA.

The best news is that in addition to all the major companies and the digital native companies, smaller companies are also ready to invest in data mining operations. With all these comes a prediction of an increase of about 30% in the number of data science jobs over the last year. It is indeed the best time to improve your expertise in data science.

Data Science With Python Syllabus

Introduction to Python

Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels - Date & Time Values
Basic Operations - Mathematical - string - date
Reading and writing data
Simple plotting
Control flow & conditional statements
Debugging & Code profiling

Scientific distributions used in python for Data Science

Numpy, scify, pandas, scikitlearn etc

Accessing/Importing and Exporting Data using python modules

Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects - subsetting, methods
Exporting Data to various formats
Important python modules: Pandas

Data Manipulation - cleansing - Munging using Python modules

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

Data Analysis - Visualization using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)

Basic statistics & implementation of stats methods in Python

Basic Statistics - Measures of Central Tendencies and Variance
Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
Inferential Statistics -Sampling - Concept of Hypothesis Testing
Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
Important modules for statistical methods: Numpy, Scipy, Pandas

Machine Learning

Predictive Modeling - Basics
Introduction to Machine Learning & Predictive Modeling
Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)

Machine Learning Algorithms & Applications - Implementation in Python

Linear Regression
Segmentation - Cluster Analysis (K-Means)
Decision Trees (CART/CD 5.0)
Support Vector Machines(SVM)
Other Techniques (KNN, Naïve Bayes, )
Important python modules for Machine Learning (SciKit Learn, scipy, etc)

Deep Learning

Artificial Neural Networks(ANN)

Data Science With R Programming Syllabus

Introduction to R Programming

History of R
Features of R

R studio

Introduction to R studio
Advantages of using R studio
Installing R in the system
Setting up work space
Windows in R studio

Packages

Introduction to packages
Installing and loading packages
Managing packages

R Programming Syntax

Command prompt
R script file
Comments

Object Types

Vectors
Matrices
Arrays
Factors
Data Frames
Lists

Vectors

Types of vectors
Character
Numeric
Integer
Logical
Complex
Raw
Creating vectors
Creating multiple elements vectors
Accessing vector elements
Manipulating vectors

Matrices

Creating matrices
Accessing elements of a matrix
Matrix processing and computation

Arrays

Creating arrays
Naming columns and rows
Accessing array elements
Manipulating array elements

Lists

Creating a list
Naming list elements
Accessing list elements
Manipulating list elements

Factors

Creating factors
Factors in data frame
Changing the order of levels
Generating factor levels
Converting characters to factors

Data frames

Creating data frames
Difference between a matrix and a data frame
Subsetting data from data frame
Extract data from data frame
Joining columns and rows in a data frame
Merging data frames

Converting data types using functions

As.numeric ,as.character,as.matrix,as.data.frame etc.

Checking data types using functions

Is.character, is.numeric,is.matrix,is.data.frame etc.

Operators

Types of operators

Arithmetic operators
Relational operators
Logical operators
Assignment operators
Miscellaneous operators

Decision making statements

If statement
The if else
Switch statement

Loops

Repeat loop
While loop
For loop

Loop control statements

Break statement
Next statement

Functions

Function definition
Function components

Types of function

Built-in function
User-defined function

Types of built in functions

Character/string functions
Numeric and statistical functions
Date and time functions

User defined functions

Creating a function
Calling a function
Lazy evaluation of function

Overview of Important R Packages

dplyr
stringr
ggplot
ggplot2

Importing External Data

Text files
CSV files
Excel files
Xml files
Web data
Databases

Statistical Analysis

Descriptive statistics
Measure of central tendency
Inferential statistics
Hypothesis testing
Exploratory data analysis

Data Visualization

Pie charts
Bar charts
Box plots
Histograms
Line graphs
Scatter plots

Introduction to Machine Learning

Origin and the history of machine learning
Differences between AI and machine learning
Differences between data science, statistics, data mining and machine learning
Applications of machine learning
Limitations of machine learning
Machine learning is the future
Implementing machine learning in R

Machine Learning Process

Collecting data
Pre-processing and preparing data
Exploring data
Choosing a model
Training the model
Evaluating the model
Improving the performance of model

Machine Learning Theories and Algorithms

Machine learning theories
Machine learning theories to algorithms
Meaning of algorithm
Importance of algorithms in machine learning
Components of machine learning algorithm

Types of Machine Learning Algorithms

Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning

Supervised Learning Tasks and Algorithms

Classification

Nearest neighbor (non-parametric /instance-based)
Naive bayes theorem (parametric /probabilistic)
Decision trees (non-metric /symbolic)

Prediction

Linear regression

Classification/prediction

Artificial neural networks (neural networks and deep learning)
Support vector machines (non-probabilistic)

Unsupervised Learning Tasks and Algorithms

Pattern detection

Association rules (rule based learning)

Clustering

K-means clustering

Ensemble Methods/Meta Learning Algorithms

Random forest

Common Machine Learning Packages in R

Rweka
C50
Psych
Class
Kernlab
Tm
Neuralnet

FAQ

  • Data Science Course at FITA is designed & conducted by Data Science experts with 10+ years of experience in the BI & Data Science domain
  • Only institution in Chennai with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 15,000+ students trust FITA
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies

We are happy and proud to say that we have strong relationship with over 600+ small, mid-sized and MNCs. Many of these companies have openings for data scientists. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.

You can contact our support number at 98404 11333 or directly walk-in to one of the FITA branches in Chennai or Coimbatore

The syllabus and teaching methodology is standardized across all our branches in Chennai. We also have a FITA branch in Coimbatore. However, the batch timings may differ according to the type of students who present themselves.

We are proud to state that in the last 7+ years of our operations we have trained over 15,000+ aspirants to well-employed IT professionals in various IT companies.

We have been in the training field for close to a decade now. We set up our operations in the year 2012 by a group of IT veterans to offer world class IT training.

We at FITA believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each data science batch to 5 or 6 members.

Our Data Science faculty members are industry experts who have extensive experience in the field handing real-life data and completing mega real-time projects in related areas like Big Data, AI and Data Analytics in different sectors of the industry. The students can rest assured that they are being taught by the best of the best from the Data science industry.

Our courseware is designed to give a hands-on approach to the students in Data Science. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.

We accept Cash, Card, Bank transfer and G Pay.

In India, big companies like IBM, Accenture, Flipkart, Amazon, Myntra, CTS, Capgemini, and other MNCs, offer job opportunity for Data Scientists with handsome salary package in the range of 8-16 lacs per annum. Data Scientists are  hired to solve the problems in the company utilizing the whole dataset of the organization. The insight provided by a data scientist regarding the problem is the value you add to the organization, and for those valuable insights, Data scientist is one among the highest paid jobs of the twenty-first century.

Mostly these companies recruit experienced data scientists and for anyone to begin a career in data science requires a lot of skills to get placed in the job. According to recent estimates, there is a huge demand for data scientists in various fields. As a data scientist, one must possess exceptional skills in Python, R-Programming, Probability, Statistics, Statistical models, visualization tools like Tableau, communication,  data assimilation and many more. Being a data scientist does not limit oneself to the technological aspects of the business alone because as a problem solver & strategist, your insights using the data are vital in steering the company on the path of development.

Skilling yourself with the desired skills and preparing for big companies as a Data scientist is not a far reach, as FITA provides best-in-class training for the Data Science course in Chennai with branches located at Velachery, Anna Nagar, T Nagar, and Thoraipakkam (OMR). Call 98404-11333 for more details to learn Data Science Training in Chennai and Coimbatore as well.

As a Data scientist, you are expected to perform various tasks on technological tools and high-level programming language like Python or R for identifying the best performing model for deriving business strategies. Arriving at the best solution requires hands-on experience in working with these tools. The right balance of practical and theoretical knowledge provided in FITA enables learners to hone their skills. We train students with advanced concepts in R like Exploratory Data Analysis & Machine Learning with R, as these are considered an un-written prerequisite by huge organizations.  FITA has been instrumental in transforming students and IT professionals into the industry-ready workforce with 100% placement support and continued guidance even after course completion. Being certified through Data Science training in FITA will add value to your profile, and with the skills acquired in the training process, your dream job as a Data Scientist is close at hand. Enrolling yourself into our Data Science Course in Chennai at FITA will act as a launchpad for your rocketing growth in career.

Resume acts as the virtual face of a candidate, which should highlight the key skills and capabilities of the candidate even without one's physical presence. Anyone who possesses skills like Python & R-Programming, Database design & management, SQL, Tableau, Data Mining, etc., mentioned in the resume is more likely to be interviewed for the role of a Data Scientist. Based on WEF’s “Future of Jobs 2018 report”, Data Scientists and Data Analysts are among the few roles which will see a huge rise in demand in the period up to 2022.

Hard skills (math, logical analysis, troubleshooting, Tableau, data warehousing, pattern & trend identification, etc.) and soft skills (Communication, report writing, critical thinking, creativity, teamwork, etc.) are a few of the skills sought in a person aspiring to be a Data Scientist. If you have done any projects in Data Science during your graduation, mention the objective of the project, tools & techniques utilized, your contribution to the project briefly in the resume. Any certification course in Data Science will add a feather in your cap, cause of the hands-on experience on the tools(Python, R, SQL ) trained during the course. Data Science certification course in Chennai, provided by FITA helps aspiring candidates to land in their dream job and excel in it due to strengthened fundamentals during the course. FITA provides best-in–class training for the Data Science course in Chennai with branches located at Velachery, Anna Nagar, T Nagar, and Thoraipakkam (OMR).

Here is how a typical day of a data scientist looks. I am writing about a data scientist who worked for a project at Amazon.

The employee was tasked with a wide and open-ended project. He was given huge volumes of equipment failure data in AWS Data Centres. Amazon operates and owns plenty of data centers around the world, which stores information when any equipment fails. 

He had access to a huge amount of data and working on infinite possibilities using engineering to arrive at a solution. In a few months, he produced a report assimilating the humongous data and provided insights on the important facets of data. When the report was provided with much clarity and confidence in the findings in the report, he was satisfied with his role as a data scientist.

The professional life of a data scientist is like a roller-coaster ride which gives you high when you arrive at vital insights to the organization with the skills you possess as a data scientist.

Data Science certification course in Chennai, provided by FITA helps aspiring candidates to land in their dream job as data scientist and excel in it due to strengthened fundamentals during the course.

Airbnb has optimized its hiring process for data scientists in recent years. They have reduced lots of one-to-one interviews and now following a systemic approach to test your skills as a data scientist.

Airbnb prefers candidates with experience in the field, and entry-level candidates with excellent problem-solving approaches and skills in technical tools like python, R-programming, and Tableau are more likely to be hired. Keeping the resume updated with the skills you possess as a data scientist, leads you to the next level of the hiring process where the company provides you with a data set and asks a few basic questions. 

In the next stage, the candidate sits along with the team and gets access to in-house data and asked to solve a broader question. The team also supports the candidate and encourages them to ask more questions to provide deep learning. At the end of the day, a  candidate has to present their methodologies and findings in front of a small team, where their data processing skills, modelling ability from the processed data, and communication skills are scrutinized.

On clearing these rounds, a candidate is interviewed by two business partners to assess the candidate ability to work collaboratively. Also, the candidate is interviewed for assessing their orientation towards the core values and mission of Airbnb.

Data Science course in Chennai, provided by FITA helps aspiring candidates to land in their dream job as a data scientist and excel in it by strengthening the fundamentals during the course. FITA provides best-in-class training for the Data Science Training in Chennai with 100% placement assistance and continued guidance even after course completion.

Machine Learning (ML) is an important part of Data Science but knowledge in ML algorithms alone does not make a candidate eligible to become a data scientist in techno-giants like Google, Microsoft, Facebook.

Here are some common aspects expected from candidates by huge companies. 

A candidate is expected to know programming languages like Python or R.

Statistics is one of the important things to be known by a data scientist to determine important aspects of the huge datasets. 

Regarding ML, one must possess a clear knowledge of K-nearest neighbours, ensemble methods, random forests, and unsupervised learning. 

Candidates are also expected to possess skills in data processing, data wrangling, data visualisation, and data engineering tools and techniques.

Apart from knowledge in technical aspects, a data scientist is expected to have problem-solving skills and provide vital insights for the companies development from the dataset they work on.

A data scientist is also expected to have business acumen, cause they are expected to be an important member in developing business strategies.

Candidates who are willing to make a career in data science can enroll yourself in the Data Science course in T Nagar, provided by FITA. FITA helps aspiring candidates to land in their dream job as a data scientist and excel in it by strengthening the fundamentals during the course. FITA has various branches in Chennai located at Velachery, OMR, Tambaram and Porur. Candidates nearby these locations can enroll in our Data Science Training in Chennai at FITA.

Data has become an important asset in businesses these days. The organizational data and consumer data helps the organization in providing valuable insights on the preferences of the customer and helps in improving the processes involved in the business. Data science has enabled many companies to tap into their data to realize, analyze and predict the outcomes of the business. Machine Learning has played a major role in creating predictive mathematical models (supervised/unsupervised) using the existing input data to predict outcomes and also enables one to build a model for desired outcomes. ML utilizes various methods like Artificial neural networks, Decision trees, Bayesian networks and various other models to predict the outcomes.

ML has its application in finance, education, automobile, aviation, robotics,  healthcare, banking, biotechnology, pharmaceuticals, DNA sequencing, etc.

Learning ML techniques and equipping with skills in ML tools, will help aspiring Data Scientists to get placed in their dream job. Due to its wide applicability, many companies are hiring people who possess ML skills and provide them with a handsome package. ML has started making footprints across sectors, and with ML skills, one gets an added advantage to be hired by the majority of the companies. 

Aspiring students can enroll in the Data Science course in Chennai, provided by FITA. Training is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their career as data scientists. 

Enquire for more details about the Data Science course in Chennai at FITA.

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What do you infer from logistic regression?

Logistic regression is a statistical model which utilises logistic function for modelling dependent variables which are binary in nature. Logistic regression is deployed for the prediction of binary outcome from any linear combination for the predictor variables.

Define Selection Bias.

Selection bias describes a situation where the sample analyzed differs from the whole set of data in important aspects for which they are analyzed, resulting in biased conclusions. This is a type of error, which occurs during the decision process. It is also known as the Selection effect.

The various kinds of selection bias consists of:

Sampling bias- This occurs when the samples present in a population are non-random.

Time interval- The termination of the trial occurs at an early stage for extreme value.

Data- When particular data subsets are selected for the supporting of conclusion over arbitrary grounds.

Attrition- It is also a type of selection bias that is caused due to attrition.

How is data cleaning important in analysis?

For each data analysis process, data cleansing is very important though it may consume more time. Also, data is accumulated from various sources to convert the whole dataset into a specific format, which can be processed by the data scientists. There might be data that are duplicate, redundant and unworthy to the analysis being carried out.

What do you infer from Normal distribution?

Normal Distribution, also known as Gaussian distribution is a distribution in probability where the data is spread symmetrically about the mean, which implies that Data closer to the mean occurs more frequently than the data farther from the mean. Data is distributed about the central value irrespective of its reach that may occur in the form of a bell curve.

Characteristics of normal distribution are:

  • Unimodal
  • Bell
  • Symmetrical
  • Asymptotic
  • Mean, median and mode

Differentiate between Systematic and Cluster sampling.

The major difference between Systematic and Cluster sampling is the manner in which they pick sample from the population.

Systematic Sampling

  • Here, Sample is picked from the population from any random starting point and at regular intervals from the starting point depending on the size of the population.
  • Provide accurate results
  • Probability sampling method
  • This is used when the population contains important units throughout and while making important decisions based on the sampling.

Cluster Sampling

  • In Cluster Sampling, the population is divided into clusters and a random sample is taken from each cluster.
  • Results are less accurate compared to systematic sampling
  • Random sampling method
  • This can be used when the population is large and it reduces the time and money spent.

Point out the difference between underfitting and overfitting.

Both of these terms denote the state of the statistical models or machine learning algorithms which hampers reliable predictions from the generated data.

When we consider overfitting, the statistical model characterizes noise or inaccurate data rather than the underlying relationship. Here, the model is fed with tons of data exceeding the capacity of the model. Its usage is visualized when there is a presence of a complex model that comprises of many parameters related to the counting of observations.

The Underfitting process occurs when any statistical model is unable to grasp the fundamental trend of data. This happens when there is insufficient data for building an accurate model.

How is Supervised learning different from unsupervised learning?

Supervised learning is a process in which an algorithm is learned from the training dataset. Here we use an algorithm to map the input and output using the available input/output variables in the training dataset. This mapping enables to predict outcome variables when new input data is fed.

In Unsupervised learning, there is no training dataset and we only have input data that contains hidden patterns and distributions of data. It aims at modeling the hidden patterns in the data to understand more about the data.

Data scientists predominantly use both types of learning where unsupervised learning is used to process the data during exploratory analysis and to train supervised learning algorithms using the generated data set from unsupervised learning. Supervised learning is used for financial analysis, training neural networks,  forecasting, facial recognition, and various other processes.

Explain univariate, bivariate and multivariate analysis.

Univariate analysis is considered to be the simplest form of statistical analysis, which utilizes only one variable data set. It is used to describe, summarise and find patterns in the Data.

Bivariate analysis tries to build a relationship between the available two-variable data sets.

Multivariate Analysis deals with acquiring knowledge of multiple variables to understand the aftermath of the presence of variables over the responses.

What do you mean by box cox transformation in the regression model?

The response variable involved in regression analysis may not satisfy numerous assumptions of any ordinary square regression. A Box-Cox transformation helps to convert non-normal dependent variables into a normal shape. If the data is abnormal, applying Box-Cox transformation can help to run a wide range of tests as normality is an important criterion for many statistical techniques.

What do you infer from Eigen  vectors and Eigen  values?

The Eigenvectors are widely used for the understanding of linear transformation. The user calculates Eigenvectors for the covariance matrix during the data analysis. They are considered to be the directions for a specific linear transformation by the act of flipping, stretching or compressing.

The eigenvalue is known as strength for the change in direction of Eigenvector.

List few assumptions related with linear regression. The presumptions regarding linear regression are mentioned below:

  • The relationship that persists amidst dependent variables and regressors is considered to fit the data that has been actually created by the user.
  • The distribution of error happens to be normal as well as being independent.
  • The multiple correlation is very minimum amidst the variables.
  • Variance all around regression line is identical for every predictor variable.

What do you infer from exploding gradients?

They are a sort of error gradients that gets accumulated while training a neural network algorithm resulting in huge updates for the neural network. This accumulation causes the neural network weights to increase abnormally during the process of training thus providing results in NaN values. Gradients are used to train and update the network weights, which is beneficial when the gradients are small and controlled. If the magnitude of the error gradient accumulates it causes instability in the neural network algorithms spoiling the purpose of training.

What do you infer from SVM machine learning algorithm?

Support Vector Machine(SVM) can be deployed for  Classification and Regression. SVM attempts to find a hyperplane (in N-dimension space) that can classify every single feature with specific coordinates. This also makes use of hyperplanes to separate various classes based on the kernel function. A hyperplane is just a boundary between two or more classes of data.

How will be statistics used by Data scientists?

Statistics is the real soul of Data Science. Statistics is the most important subject that provides the tools and techniques to structurize data and find deeper insights from data. Its main usage will be seen in identifying the patterns and conversion of raw data to business insights. This also aids the data scientists to develop excellent ideas that are expected by the customers. It is easier for them to analyze the interest, consumer behavior, engagement, retention along with the perspective statistics. This also helps in building up of data models for the validation of certain interferences. Every aspect is possible to be converted to any business proposition.

What do you mean by Random Forest? And explain its working?

It is a flexible method deployed in machine learning to perform classification and regression tasks. It is used in dimensionality reduction, outlier values, and treats missing values. This is considered to be a kind of ensemble in which weak models are combined to create a powerful model. It is possible to grow several trees against any single tree for the classification of new objects that are based on attributes.

What do you mean by Extrapolation and Interpolation?

Both of these terms are considered very important in statistical analysis. Extrapolation is done to estimate value by extending a known sequence of values beyond the known area to infer implicit information from the provided information. Interpolation is something used for the determination of specific value that lies amidst fixed values that is useful when there are two extremities of a specific region.

Differentiate between Data modelling and Database design.

Data modeling is the initial step in the process of Database design. This helps in the creation of a conceptual model that is based on connection amidst different data models. It consists of the transition of the conceptual stage to a logical model. Database design is deployed for the plotting of a database that aids in the creation of  a precise data model. This consists of physical design choices along with storage parameters.  

List some of the disadvantages of linear model.

The most important drawbacks of this model are:

  • The expectation of linearity errors.
  • Outcomes count cannot be calculated.
  • Overfitting problems that are impossible to resolve.

When partial data is given by the user in any search engine, how is it possible to predict the search?

It can be predicted by the previous frequencies of various word sequence, conditional probabilities can be constructed for the next sequences, which can be shown up. The order that consists of the highest conditional probabilities will be shown on top of the suggestions list. For the additional improvisation of the mentioned algorithm, the user can add weight over the past series that appeared in the recent time.   

What do you infer from Boosting ?

This is an ensemble methodology for attempting in the creation of a strong classifier with the help of numerous weak classifiers primarily to reduce bias and variance. The performance of every tree is used for weighing how much attention is to be given to every next tree that is built-up. Every model is created in a follow-up manner, further updating weights.

What are all the pre-processing steps that are highly recommended?

List the pre-processing stages which are recommended.

  • Missing value treatments
  • Outlier Analysis
  • Feature engineering
  • Structural Analysis


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