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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What is it Like to Be a Data Scientist in 2021?

Ram Dewani
Ram Dewani Last Updated : 06 Jan, 2021
7 min read

Overview

  • The rise in demand for data scientist continues in 2021 too
  • Understand what is it like to be a Data Scientist in 2021

 

Introduction

From big E-commerce companies like Amazon, Walmart, to social media giants Facebook and Snapchat – all the way up to hospital management – everyone is hiring data scientists! But what is it that makes this role the “Sexiest Job Role of the 21st century”? We will discuss each and every aspect of this job in this article.

Data Scientist 2021 featured image

If you are someone that is excited by this job role and wants to create a future in this field in 2021 then this is the place to be! Don’t worry if you think that the coronavirus has killed the job requirement of a data scientist instead it has made everyone realize the power and importance of predictive algorithms!

If you are beginning your journey in the field of data science, this comprehensive data science learning path for 2021.

The learning path for 2021 is the ultimate and most comprehensive collection of resources put together in a structured manner. This learning path is for anyone who wants to make a career in data science. So whether you are a fresher, have a few years of work experience, or are a mid-level professional – this data science learning path is for you. 

 

Table of Contents

  1. Who is a data scientist?
  2. Other Data-based roles
  3. Qualities of a data scientist
  4. What skills to master in 2021 to become a data scientist?
  5. Salary of a data scientist

Who is a data scientist?

Data science is a combination of data analysis, algorithmic development and technology in order to solve analytical problems.

who is a data scientist

A data scientist works on complex and specific problems to bring non-linear growth to the company. For example, making a credit risk solution for the banking industry or use images of vehicles & assess the damage for an insurance company automatically.

In simple words, a data scientist is a problem solver who uses data to solve problems that create business value.

A typical data science project lifecycle looks like this:

  • Converting the business problem into a data problem
  • Hypothesis generation
  • Data collection or extraction
  • Exploratory Data Analysis and validating hypotheses
  • Data modeling
  • Model deployment
  • Presenting your work to the final user/client/stakeholder

But a data scientist may not be involved in all of these steps. Let’s look at some of the data science-based roles.

Other Data-Based Roles

Data Engineer

who is a data engineer

He would Implement the outcomes derived by the data scientist in production by using industry best practices. For example, Deploying the machine learning model built for credit risk modeling on banking software.

Data Engineers are responsible for storing, pre-processing, and making this data used for other members of the organization. They create the data pipelines that collect the data from multiple resources, transform it, and store it in a more usable form.

Some of the most commonly used tools by data engineers are SQL, NoSQL databases, Apache Airflow, Spark, Amazon Redshift, etc.

You can read Data Engineering articles here and see if your interests correlate more to data engineering.

 

Business Analyst

Business analytics professional skills

Run the business and take decisions on a day-to-day basis. He’ll be communicating with the IT side and the business side simultaneously.

Business Analytics professionals must be proficient in presenting business simulations and business planning. A large part of their role would be to analyze business trends. For eg, web analytics/pricing analytics.

Some of the tools used extensively in business analytics are Excel, Tableau, SQL, Python. The most commonly used techniques are – Statistical Methods, Forecasting, Predictive Modeling, and storytelling.

You can read the business analytics articles here.

So you think that you can become a data scientist? Let’s looks at some of the qualities of a data scientist!

Qualities of a data scientist

Before choosing data science as your field, you must see if it matches your passions, career goals, and make sure it makes you happy in the long term. Let us look at a few of them –

  1. Love Number Crunching – Are you crazy about numbers? Like, are you up for a puzzle, guess-estimates at any time of the day? Are you naturally attracted to probability and statistics? Part of being a data scientist is to frequently crunch numbers, if you love it then you are in luck!
  2. Enjoy solving unstructured problems – It is very rare that a data scientist actually encounters a structured problem statement, instead he deals with unstructured data. Are you someone that aces in this area?
  3. You are curious – asking why comes naturally to a good data scientist. Some of the best data scientists would stop anyone and ask for a rationale if they are not clear – Why did you ask this question? What was your thought process? Why do you assume so? are just a few examples of these questions!
  4. Crave problem-solving – Data Scientists require a knack for problem-solving. Most of the problems businesses would face would be unique to them and it would take a smart solver to solve them.
  5. Enjoy deep research – A great data scientist is always digging deep to understand the hidden secrets of data. You need an outlook of a researcher to be a good data scientist. When was the last time you spent hours and hours immersed in solving a problem? Can you do that again and again?
  6. Love telling Stories – A data scientist needs to be a fluid presenter. What is the use of all the hard work, if he is not able to influence his stakeholders? Communicating with data and presenting stories backed by data is one of the most important elements in the life of a data scientist.

What skills to master in 2021 to become a data scientist?

Data Science Toolkit –  The most important skill to gain at the beginning of your journey as a data scientist is the basics of data science and machine learning. Start from the most common and frequently used data science tools – Python and its libraries such as Pandas, NumPy, Matplolib, and Seaborn. 

Data Visualization and SQL – As you have cleared the basics, you need to begin with the most crucial skillset of a data scientist. Familiarize yourself with different data visualization tools and techniques such as Tableau. During this time, you should also begin your SQL journey.

Data Exploration – The data is hidden with important information. Bringing out this information in the form of insights is data exploration. It is the most essential skill to learn how to explore your data with Exploratory Data Analysis (EDA). Along with this, you will also need to understand the important concepts of statistics required to become a data scientist. 

Basics of Machine Learning and the art of storytelling – Now let’s get down to actual machine learning! After gaining all the above skills, it’s time for to you start your Machine Learning journey. In this duration, you will need to cover basic ML techniques and the art of storytelling using Structured thinking. 

Advanced Machine Learning – Done with basics? It’s time to turn up the notch! You are ready to cover advanced machine learning algorithms. You will also learn about feature engineering and how to work with Text and Image data. 

Unsupervised Machine Learning – Dealing with unstructured data can be challenging so let’s jump into the solution! It is time for you to learn about unsupervised machine learning algorithms like K-Means, Hierarchical Clustering, and finally deep dive into a project!

Recommendation engines – Curious how Netflix, Amazon, Zomato give such amazing recommendations? It is time for you to delve into recommendation systems. Learn different techniques to build recommendation engines. Learn using different projects.

Working with Time Series Data – Organizations around the world depend heavily on time-series data and machine learning has made the scenario even more exciting. In this duration, you will learn how to work with Time Series data and different techniques to solve time series related problems.

Introduction to Deep Learning and Computer Vision – Deep Learning and Computer Vision is at the forefront of the most happening projects in the field of AI be it Self driven cars, mask detection cameras, and more. In this time, you will start your journey in the field of Deep Learning. You will learn basic deep learning architectures and then solve different computer vision projects.

Basics of Natural Language Processing – Do you wonder how Social media giants like Twitter, Facebook, Instagram process incoming text data? It is time to move your focus to the field of Natural Language Processing (NLP). Here you will learn more deep learning architectures and solve NLP related projects.

Model Deployment – What is more essential than building a data science model? Deploying it! Now finally you must be aware of model deployment. Learn different ways to deploy your models. You’ll get to spend time on exploring streamlit for model deployment, AWS, and also get to deploy the model using Flask.

 

A Data scientist’s salary

Making a career switch to data science for getting a salary bump is entirely justified. However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post-transition.

Taking figures from the popular and relatively accurate website called Glassdoor, this is what the salary situation looks like for a data scientist:

data scienctist base salary in 2021

As you can see, the average salary in 2020 is approximately INR 10,00,000 per year.

If you bring a bit more experience to the table and you have relevant domain experience, you might look at a more senior role (though this is a bit rare if you have no prior data science experience):

data scientist experienced salary

As we said, it comes down to how relevant your previous experience is. More often than not, if you are transitioning from another role to data science, you’ll be looking at the first graph.

 

End Notes

To summarize, Data Science is the most emerging field today and data scientists are creating a better future for humanity. Are you someone that is attracted to this field? I have mentioned all the things you must know before building a career in data science in the year 2021.

Happy Learning!

data scientist 2021
Ram Dewani
Ram Dewani

Product Growth Analyst at Analytics Vidhya. I'm always curious to deep dive into data, process it, polish it so as to create value. My interest lies in the field of marketing analytics.

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Here’s What You Need to Know to Become a Data Scientist!
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