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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

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

Ram Dewani
Ram Dewani Last Updated : 25 Jan, 2021
6 min read

Overview

  • Being a data scientist has become a prestigious career path in 2021
  • Understand the whole ball of wax around being a data scientist in 2021

 

Introduction

Data Science is one of the most coveted job roles in the industry right now. There’s no doubt it. But if you are looking to start a career in this field, more often than not, the conversation goes something like this –

“I want to have a high growth role and a role with a high pay-out which is exciting and helps me solve big organizational or global problems. ”

Your friend replies – “I have heard data science is a buzzword, it is a high growth job, with a high salary band, and gives you the power to solve crucial problems. You should definitely check it out!”

app_developer_data_science

A majority of people know that data science is exciting, an upcoming field that has a high salary band but only a few know about the depths of the field. So in this article, I am going to talk about some of the fundamental discussion points that you must know if you are starting out in this field.

If you are looking to become an industry-ready professional with flattering project experience along with a personalized learning path and guidance from industry leaders? The AI and ML Blackbelt program helps you to gain all the relevant knowledge with 1:1 mentorship assistance, a personalized and comprehensive learning path, and job assistance!

 

Firstly, Who is a data scientist?

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

data scientist 2021 skills

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.

Do you want to become a data scientist in 2021? We have made a comprehensive month-on-month learning path just for you! It contains a structured roadmap to becoming a data scientist along with all the resources you will require to fulfill this journey – 

 

What are the key skills required to excel in a hands-on data science role?

data scientist 2021 role requisites

Data science is a multi-faceted role. There is no one-size-fits-all approach to learning data science. Having said that, there are a few core skills you will need to pick up to make a successful career transition to data science.

Here are the key skills you would need:

  • Programming Language
  • Statistics
  • Machine learning concepts
  • Structured thinking
  • Ability to work with Databases
  • Communication skills

Apart from these core skills, there are other skills you should be aware of, such as:

  1. Deep Learning concepts
  2. Big Data
  3. Software Engineering
  4. Model Deployment

The role of a data scientist requires good quantitative skills as well. So if you are coming from a non-quant background make sure to strengthen your quant skills!

Here are some great resources to learn machine learning skills –

 

Career Path as a data scientist

A lot of data science professionals and enthusiasts get confused by the data science career lifecycle. It is probably due to data science being a relatively new field and organizations globally are trying to figure out the “Sexist Job of the 21st century” as they incorporate data science projects in their organizational priorities.

Let’s check out a typical career path as a data scientist –

career path as a data scientist

Caution: The data science career path are losely defined in the industry. The exact career path can depend on the maturity of your organization in data initiatives.

As we can observe in the above career path, being in a data science role throughout the journey can lead up to a position of a data science leader which is about identifying and formulating business problems, stakeholder management, and forming the overall data strategy of the division or the organization.

As mentioned above, since the role is relatively in its nascent stage, a lot of professionals apply their learning to move on to entrepreneurial path or product roles. Data Scientists gain plenty of domain knowledge over their experience and hence gain a lot of insightful knowledge about the product and industry-leading them to do well as entrepreneurs or product managers.

The big salary question – what can you expect from this role?

data scientist salary

Are you trying to make a career switch to data science for getting a salary bump? Well, the move 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:

average salary data science

average base pay data science US

 

As you can observe, in India, the average salary of a data scientist is approximately INR 10,00,000 per year. Whereas, the average salary for the same in the USA is a whopping $134,000 per year. (there is definitely a big gap there!)

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):

senior data scientist salary in india

 

senior data scientist salary in USA

As a senior data scientist, you will be looking around an average salary of approximately INR 19,00,000 per year in India. In the USA, you can expect an average salary of a whopping $134,000 per year.

As we said, it comes down to how relevant your previous experience is. More often than not, as a person transitioning from a non-data role to data science, you’ll be looking at the first graph whereas, if you have been previously working in the data industry, you might be eligible for a senior data science role, which is the second graph.

 

How to become an industry-ready data science professional?

Data Science is one of the most exciting and upcoming roles in the industry with a lot of growth and financial potential. As we have noticed in so many reports, there are plenty of data science jobs out there but there’s a big caveat! There are not as many skilled data science professionals for these job roles.

A recent Gartner report projects that there will be approximately 2.3 million new jobs created by the year 2020 in the field of Artificial Intelligence and Machine Learning.

The AI and ML Blackebelt Program are one of the few programs that not only educate you but make you industry-ready. Some of the key special points about the flagship program are –

  1. 1:1 mentorship calls with industry practitioners
  2. Comprehensive and personalized learning path
  3. Prepares you for AI and ML jobs

So are you ready to take on the most coveted job role of the century?

 

End Notes

In this article, we discussed some of the key attributes of being a data scientist, the role, skills, salary, and finally how can you become an industry-ready data science professional with the help of the AI and ML Blackbelt program.

I hope you got to learn about this exciting industry and found this article insightful. I will be happy to hear your thoughts in the comments.

 

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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