How To Start a Career In Data Science  

Presently, Data Science is a highly sought-after and promising career option for students who are looking to get into this exciting career. In the near future, it is possible to see an upsurge in jobs with a minimum of 28%, which would be around 11.5 million new positions in the Data Science field.

Data Science is the disciplinary approach of interpreting and analyzing data, both structured and unstructured. What is Data? Data preparation is the process of cleansing, aggregating, and manipulating it to be ready for specific types of tasks.

Data Science mainly relies on techniques such as statistics, mathematics, analytics, programming, and scientific methods. And many more industries that depend on Data Science are Banking, finance, construction, transportation, communication, media and entertainment, education, and government. Some of the organizations hiring Data Science are Accenture, IBM India, Cognizant Technology, etc.

To work in these organizations and get wider opportunities, you can join Data Science Online Training at FITA Academy, which provides industry-relevant skills required for a professional Data Scientist under Expert guidance.

To help you choose the right career path, we have explained in detail the importance of choosing the correct job role according to your skills and knowledge. Choose What You Need To Learn:

There are a lot of roles in the data science industry. Data Analyst, Data Scientist, Senior Data Science Engineer, Data Science Consultant, Data Science Application, Developer, Big Data Engineer, Software Engineer, and Data Science Manager. You can choose any one of these roles to explore in the Data Science industry.

Why is choosing the right field so important? Because if you are a software engineer, it would not be difficult for you to shift into a Data Engineer. So getting into the proper role will enhance your career path, and it tells what you want to become. To clear your confusion, we have a few suggestions for you to take up the right path.

  • Determine what you desire and are good at, then select a career that fits your field of study
  • You can ask relevant questions to the people in the industry.
  • You can take mentorship from people to find out what each of the roles entails.
Choose Tool And Language:

There are many programming languages to choose from, like Python, R, Java, C/C++, or Julia. But Python has highly ranked in the Data Science platform and commonly used programming language that helps you communicate with machines. Python is used for all sectors because it has multiple data science libraries and high support for deep learning.

Honestly, Python is easier to perform Machine Learning tasks due to the availability of libraries, easy to learn, Handling Capabilities, and Graphical Capabilities. Python also helps in building websites, machine learning algorithms, and almost in every industry. Many popular companies use Python for their daily use. such as

  • Google, NASA, Amazon
  • Social networking sites like Instagram, Reddit, etc
  • Media like Netflix and Spotify
Primarily, data scientists use python libraries for their daily tasks like:
  • Pandas-data cleaning and analysis
  • NumPy, Scipy -basic statistical tools
  • Seaborn, Matplotlib, -data visualization

To learn Python, you should focus on the following: data types, data structures, imports, functions, conditional statements, comparisons, and loops.

Learn Machine Learning: learn machine learning  

In Machine learning, Scikit-learn is a key library for the Python programming language. Scikit-learn is a set of Machine learning tools that include mathematical, statistical, and general-purpose algorithms that serve as the foundation for a variety of Machine learning technologies.

We may use scikit-learn to create a variety of Machine learning models for regression, classification, and clustering, as well as statistical tools for analyzing them.

Dimensionality reduction, feature selection, feature extraction, ensemble approaches, and inbuilt datasets are also included.

Furthermore, to get the proper guidance, you can enroll in a Data Science Course in Chennai at FITA Academy, which provides the students can work on real-time projects to get industry exposure.

Understand Machine Learning By Reading Books: 

In Machine Learning, you should be familiar with Feature engineering and Feature selection. Feature engineering is the process of taking raw data and converting it in a meaningful way such that the model can understand the data better.

Feature selection is the process of reducing the input variable to your model by using only relevant data and getting rid of noise in data. Furthermore, you should also be familiar with linear regression and logistic regression in Machine learning.

If you want to excel at Machine learning, many books are available on the market to improve your skill as well as there are many books for beginners and advanced learners to enrich their knowledge of Data Science. Some of the books are:

Data Science from Scratch: First Principles with Python

Data Science from Scratch:

First Principles with Python is a well-known book written by Joel Grus. It is an introductory book on data science and Machine learning. The book targets intermediate programmers to start learning in data science and Machine learning algorithms. The book provides practical Python code, and several chapters cover mathematical concepts to give comprehensive learning to the learner. The book teaches you how to apply Machine Learning techniques to data science problems from the perspective of beginner programmers.

Doing Data Science: Straight Talk from the Frontline

Doing Data Science is authored by Rachel Schutt. The book gives comprehensive coverage on methods, algorithms, and models to use against a given data science task at hand. After reading this book, you will get clear and concise knowledge and understanding of Data Science and also be able to create ML models on your own.

Data Smart: Using Data Science to Transform Information into Insight

In this book, John W. Foreman gives practical guides to business analytics and how complex data Science concepts are demonstrated with real-world applicabilities. From this book, you can learn how to process data into actionable and valuable insights quickly and effortlessly.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron

Hands-On Machine Learning is a popular book written by Aurelien, and from the book, you can understand Machine learning concepts. This book covers theory and Machine learning algorithms and covers high content on autoencoders and reinforcement learning.

An Introduction to Statistical Learning will help you to gain a theoretical and practical understanding of Machine Learning. You can also learn many important methods for regression and classification without requiring a background in advanced mathematics.

Linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and other topics are just a few of the topics that will help you better comprehend machine learning. This book is beneficial to both beginners and advanced learners.

OpenIntro Statistics is a textbook written by Christopher D. Barr, David M. Diez, and Mine Çetinkaya-Rundelprovides provides a deeper understanding of probability and statistics.

Learn data analysis, manipulation, and visualization:

To work with data in Python, you should learn how to use the pandas’ library. In every industry, pandas are used for data analysis because of their high data structure performance. It has tools to read and publish data, manage missing data, filter data, clean up messy data, merge data sets, visualize data, and much more.

Keep Practicing and Learning:

Every day Keep practicing and learning new things about data science. You can improve your skills by reading books, reading blogs, attending conferences, or you can also enroll in data science courses to enhance your skills.

By joining the Data Science Course in Pune, you will get in-depth training in Data Science concepts and impart the necessary knowledge required for a professional Data Scientist.

Communication skills:

Communication is more important when you are working in any field. Even though you have efficient skills in technology, you need to concentrate much on communication while attaining an interview because every company needs people who can communicate well to help them succeed. So, Communication skills are essential even for a technical position like a data scientist.

Guidance Is Essential

Finally, finding the proper guidance would help you to enhance your skills in data science. It is a multidisciplinary field, so you can choose any field to explore your career opportunities. You can read many books to enhance your career, but continuous reading of the books will bore you differently, so practical plus theoretical learning will help you learn quickly. We have Data Science Courses in Bangalore for Bangalore- based students to provide practical and theoretical knowledge in Data Science.

To learn more about data science and Machine Learning, you can join Data Science Courses in Bangalore, which guides students with academic needs and provides practical and theoretical knowledge.

Summing up:

Data Science is impacted by almost every industry because every business depends on data. On the other hand Data Science is the highest-paid job of this century. Now, you would have understood how to start a career in data science. To shine in all such industries, including the IT field, you need to take an initial step to learn Data Science. As an initial step, take up a Data Science Course in Hyderabad at FITA Academy to have a proficient understanding of Data Science and be successful in your academic and professional goals.