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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

A Super Useful Month-by-Month Plan to Master Data Science in 2021

Ram Dewani
Ram Dewani Last Updated : 28 Dec, 2020
5 min read

Overview

  • Your quest to understand how to become a data scientist in 2021 ends here
  • Here’s a month on month plan that you can follow to help you achieve your goals here

 

Introduction

Data Science as a career has seen massive growth over the years. If you love problem-solving, number crunching, and data then this is probably the right field for you. There’s ac common notion that once you get into the field, you’ll be set for the future.

But, there’s a caveat here.

Data Science aspirants as well as experienced data scientists must keep themselves updated with the latest research and technology. In the field of data science, what was relevant a year ago might get extinct this year. That is how fast this space is evolving!

data_science_hacks_tips_tricks -data scientist 2021

That is why we bring to you the latest learning path to become a data scientist in the year 2021! This is a month-on-month learning path for anyone who’s looking forward to building a career in the field of data science, machine learning, and deep learning.

I will be breaking down the machine learning goals in your month-on-month plan. Let’s get started!

This learning path has been adapted from the free course – A Comprehensive Learning path to becoming a data scientist in 2021. You can check out the course to get all the resources structured according to the month-on-month plan.

 

What Topics are covered during this duration?

As I mentioned above, the skill set required by a data scientist keeps evolving. So what are we going to cover over the span of 12 months?

  1. Data Science Toolkit
  2. Data Visualization
  3. Data Exploration
  4. Basics of machine learning and the art of storytelling
  5. Advanced machine learning
  6. Unsupervised learning
  7. Recommendation Engine
  8. Time-Series data
  9. Deep Learning and Computer vision
  10. Natural Language Processing
  11. Model Deployments
  12. Projects and jobs

 

Month-on-Month Plan to Become a Data Scientist in 2021

data scientist 2021 rockstar calender

January – Data Science Toolkit and Python

Let’s begin this journey with the basics. In this month, we’ll be covering all the basics which we call the Data Science Toolkit. You’ll be understanding all the basic terms and start your journey in Python and its powerful libraries such as Pandas, NumPy, and Matplotlib.

Key Highlights of the month – 

  • What do data scientists do?
  • Python for data science
  • Pandas and Numpy
  • Matplotlib and Seaborn
  • Regular Expressions

February – Data Visualization

The secret recipe of data science is converting data into useful insights. In order to understand these insights, you must be well versed with an essential skill – data visualization, In this month, you will learn about different techniques to build great visualizations using an essential tool – Tableau. Another important skill to be covered this month is SQL. SQL is widely used for data capturing and analysis.

Key Highlights of the month – 

  • Data Visualization Tools
  • Introduction to Tableau
  • Different charts in Tableau
  • SQL for Data Science

March – Data Exploration

In this month, we will cover the art of EDA (Exploratory Data Analysis) to capture insights from our data. Another important concept to focus on this month is Statistics. It is said that statistics is the grammar of data science.

Key Highlights of the month – 

  • Importance of Statistics
  • Descriptive Statistics
  • Introduction to Probability
  • Inferential Statistics
  • Exploratory Data Analysis (EDA)

April – Basics of machine learning and the art of storytelling

Let’s get to the exciting part during this month – the basics of machine learning. Here, we will cover all the core concepts you will need to become a successful data scientist. Another bonus topic of this month is storytelling using the art of structured thinking.

Key Highlights of the month – 

  • Machine Learning Pipeline
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Naive Bayes
  • Support Vector Machines (SVM)
  • Structured Thinking: Art of Storytelling

May – Advanced machine learning

We are done with the basics and it is time to level-up our skills. In this month, we will move on to advanced machine learning topics like ensemble learning and its different variants. You will also learn about feature engineering and how to work with Text and Image data.

Key Highlights of the month – 

  • Ensemble Learning
  • Random Forest
  • Boosting Algorithms
  • Advanced Ensemble Learning
  • Hyperparameter Tuning
  • Working with Text and Image Data

June – Unsupervised Learning 

Till now we have covered supervised learning techniques. In this month we will focus on unsupervised learning and how we can utilize machine learning techniques on unstructured data. In this month, you will learn about unsupervised machine learning algorithms like K-Means, Hierarchical Clustering, and finally deep dive into a project.

Key Highlights of the month – 

  • Linear Algebra Basics
  • Unsupervised Machine Learning
  • K-Means
  • Hierarchical Clustering
  • Project: Unsupervised Learning

July – Recommendation Engines

Have you ever wondered how Netflix, Amazon, Swiggy provide you such amazing recommendations? Well, why not learn it for yourself? This month will focus on recommendation engines and also includes a project to learn hands-on.

Key Highlights of the month – 

  • Matrix Algebra
  • SVD and PCA
  • Recommender Sytems
  • Project: Recommender System

August – Time Series Data

In this month, we will deep dive into the application of Machine learning that has been used by organizations for decades – time series forecasting. The difference is that we will study simple techniques as well and move on to the advanced techniques.

Key Highlights of the month – 

  • Work with Time Series Data
  • Time Series Forecasting Techniques
  • Project: Time Series

September – Deep learning and computer vision

The ever-growing and most exciting field has to be computer vision. Its application range from image detection, classification all the way up to self-driven cars. From this month onwards, 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.

Key Highlights of the month – 

  • Introduction to Deep Learning
  • Deep Learning Architectures: MLP and CNN
  • Project: Image Classification
  • Transfer Learning
  • Object Detection
  • Project: Object Detection

October – Natural Language Processing

Natural Language Processing has been at the forefront of the recent advancements in machine learning in the last few years. The arrival of transfer learning in this field has completely transformed the space.  This month will move your focus to the field of Natural Language Processing (NLP). Here you will learn more deep learning architectures and solve NLP related projects.

Key Highlights of the month – 

  • Basics of Natural Language Processing (NLP)
  • Deep Learning Architectures: RNN, LSTM, GRU
  • Project: Text Classification

November – Model deployment

What is more essential than building a data science model? Deploying it! In this month, you will 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.

Key Highlights of the month – 

  • Streamlit for Model Deployment
  • Amazon Web Services (AWS)
  • Deploying models using Flask

December – Projects and Jobs

You are finally ready to face the world and make a living. The time has finally come to convert all your hard work into fruition! In this final month, you will do different projects and start applying for internships or jobs.

Key Highlights of the month – 

  • Apply for Internships and Jobs

data science learning path 2021

End Notes

In this article, we discussed the month-on-month plan that will lead you to become a rockstar data scientist in 2021.

You can check out this free course which consists of all the resources you will need in order to fulfill this learning plan.

I hope this helps you in achieving your dream. I would love to hear your thoughts on this below.

how to become data scientistlearning path 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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shatrughna singh

I joined the course but it is only showing first three months only. Could you tell me how to join whole course

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Amazing Blog. on demand home service app development

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