Data Scientist Roadmap 2021
By: Ashutosh Kumar
Tags: Software Development, Technology, Data Analysis, Data Science, Programming

Skills Required to Become a Data Scientist in 2021

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Data Science is the most trending topic for many students as it has become one of the high-paying jobs in the industry and it is no doubt that Data Scientists are high in demand these years.

Having said that it is still very difficult to find entry-level roles as a fresher for the Data Scientist position. But it's no doubt that Artificial Intelligence and Data Science roles will only increase in the future, therefore it is a good skill to have in today's data-driven world.

In this article I will provide you a step by step overview of how to get started with Data Science:

Step 1: Basic Fundamentals

This is actually not a difficult part but many people skip this step as it involves Mathematics. A Basic Understanding of High School Mathematics would help you to understand how the algorithms work.

This involves Linear Algebra, Calculus, Permutation and Combination, and Gradient Descent. At the end of the article, I would add some resources which would help you to learn these topics easily.

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Step 2: Programming Language

The two most popular programming languages are R and Python. You can choose any language and start practicing with the tools and libraries for Analytics. Basic Programming concepts and understanding of Data Structures are very important.

If you are a beginner I would suggest going with Python, because according to the StackOverflow survey 2020 Python is high in demand for working professionals especially in the Data Science field.

Step 3: Probability and Statistics

Statistics is the backbone of the data Industry. Any job interview you go to, your statistical skills would be tested. The topics required are:

Probability: Probability Space, Random Variable, Expectation, Variance and Covariance, Probability Distributions

Probability Space, Random Variable, Expectation, Variance and Covariance, Probability Distributions Statistics: Descriptive Statistics, Statistical Inference, Differential Statistics, Associative Statistics

You can watch this youtube tutorial by Great Learning to learn all about this topic.

Step 4: Data Analysis

In most Data Science jobs, Data Scientist spends most of their time in cleaning and manipulating the data than implementing Machine Learning.

Understanding the data and finding patterns and correlations among data is the most important part of the job. It would provide you an idea about the domain and which algorithm to choose for this particular type of problem.

In Python Data Analysis tools like ‘Pandas’ and ‘Numpy’ are very popular.

Step 6: Data Visualization

Clients and Stakeholders won’t understand the statistical language or Model prediction. Data Visualization is very important to show the patterns in a visual form using various charts and graphs to show the behavior of data and analysis.

The most popular tool for Data Visualization in Tableau. Different Data Visualization techniques involve Line chart, Boxplot, Histogram, Scatter plot, bar chart& Heatmap.

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Step 7: Machine Learning

Machine Learning is a vast topic and core of Data Science and Artificial Intelligence. There are a lot of blogs explaining what is Machine Learning and how it works (links in resources).

In this section I would provide you a checklist of what to learn:

Supervised and Unsupervised Algorithms

Classification and Regression Algorithms

Clustering, Dimensionality Reduction, Recommendation, and Association

Model Evaluation Techniques, Underfitting and Overfitting

Algorithms to learn:

Classification: Naive Bayes, Logistic Regression, Decision Trees, K-Nearest Neighbors, Support Vector Machines, XGBoost

Regression: Linear Regression, Decision Trees, K-Nearest Neighbors

Unsupervised Algorithms: K-means, Principal Component Analysis, Apriori Algorithm, Collaborative, and Content-Based filtering recommendation system.