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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

Data Science Curriculum for Self Study

Nitika Sharma
Nitika Sharma Last Updated : 23 Sep, 2023
4 min read

Introduction

Are you planning to become a data scientist but dont know where to start? Don’t worry, we have got you covered. This article will cover the entire data science curriculum for self study, along with list of resources and programs that can help you pace up the process.

This curriculum covers the basics of the tools, tricks, and knowledge you need to be a great data scientist. If you already know a little about science and statistics, you’re in a good place. If you’re new to this stuff, it might help to learn more about those things first. And if you’re pretty good at data already, this can be a quick refresher.

Remember, you won’t use all these skills in every project. Some projects need special tricks or tools not on this list. But if you get good at what’s in this curriculum, you’ll be ready for most data science jobs. And you’ll know how to learn new stuff when you need it.

Let’s begin!

Table of contents

Why Follow Data Science Curriculum?

Following a curriculum in data science is essential for structured and efficient learning. It provides you with a clear path for acquiring knowledge and skills, preventing you from feeling overwhelmed by the vastness of the field. A good curriculum ensures comprehensive coverage, guiding you through foundational concepts to advanced techniques. This step-by-step approach is a building block, establishing a solid foundation before delving into complex topics.

Moreover, a curriculum facilitates practical application. Many programs include hands-on projects and exercises, allowing you to translate theoretical knowledge into real-world skills. It tracks your progress systematically, helping you stay motivated and focused on your learning journey.

Beyond immediate benefits, following a curriculum pra epares you for the workforce. Completing a structured education in data science demonstrates commitment and proficiency to potential employers, enhancing your job prospects. Furthermore, this approach fosters adaptability, enabling you to tailor your pace based on your needs and delve deeper into challenging subjects.

In essence, a well-designed data science curriculum not only equips you with essential skills but also instills the ability to continue learning independently, a valuable trait in the ever-evolving field of data science.

Data Science Curriculum
Source: FreeCodeCamp

Data Science Curriculum for Self Study

Below is a condensed roadmap of key areas to explore when beginning your journey in data science:

Math Basics

  • Multivariable Calculus: Understand functions of several variables, derivatives, gradients, step functions, sigmoid functions, cost functions, and more.
  • Linear Algebra: Master vectors, matrices, matrix operations like transpose and inverse, determinants, dot products, eigenvalues, and eigenvectors.
  • Optimization Methods: Learn about cost functions, likelihood functions, error functions, and algorithms like Gradient Descent (including variants like Stochastic Gradient Descent).

Programming Basics

  • Choose Python or R as your primary language.
  • For Python, become proficient in libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.

Data Basics

  • Learn data manipulation in various formats (CSV, PDF, text).
  • Acquire skills for data cleaning, imputation, scaling, importing, exporting, and web scraping.
  • Explore data transformation and dimensionality reduction techniques, such as PCA and LDA.

Probability and Statistics Basics

  • Gain insights into fundamental statistical concepts like mean, median, standard deviation, variance, correlation, and probability distributions.
  • Understand hypothesis testing, p-values, Bayes’ Theorem, A/B testing, and Monte Carlo Simulation.

Data Visualization Basics

  • Recognize the importance of data type and choose appropriate visualization techniques (scatter plots, histograms, etc.).
  • Focus on components like data type, geometric choice, mapping, scaling, labels, and ethical considerations.
  • Familiarize yourself with visualization tools like matplotlib, seaborn, and ggplot2.

Linear Regression Basics

  • Learn the fundamentals of simple and multiple linear regression.
  • Explore tools for linear regression analysis in Python (e.g., NumPy, scikit-learn) and R (caret package).

Machine Learning Basics

  • Study supervised learning techniques for continuous and discrete variable prediction.
  • Explore regression, classification, and ensemble methods (e.g., Random Forest).
  • Delve into unsupervised learning, including clustering (e.g., K-means) and dimensionality reduction.

Time Series Analysis Basics

  • Discover methods like Exponential Smoothing, ARIMA, and GARCH for time-dependent data analysis.
  • Implement these techniques using Python and R.

Productivity Tools Basics

  • Become proficient in essential data science tools such as R Studio, Jupyter Notebook, and GitHub.
  • Consider advanced tools like AWS and Azure.

Data Science Project Planning Basics

  • Learn project planning, including problem understanding, dataset exploration, model selection, and evaluation.
  • Organize and structure your projects effectively for increased productivity.

Domain Knowledge

  • Depending on your interests, dive into domain-specific knowledge. For example, if you’re interested in healthcare data, understand healthcare systems and terminology.

Big Data and Cloud Computing

  • Explore technologies like Hadoop, Spark, and cloud platforms (AWS, Azure, GCP) for handling large datasets.

Natural Language Processing (NLP)

  • If interested in text data, learn NLP techniques and libraries like NLTK and spaCy.

Deep Learning

  • Delve into neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and frameworks like Keras and TensorFlow.

Data Ethics and Privacy

  • Understand the ethical implications of data science and privacy regulations like GDPR.

Resource List

Click here to explore the entire resource list for free!

Conclusion

following a structured data science curriculum is like having a reliable map for your learning journey. It helps you acquire essential knowledge and skills efficiently while building a strong foundation. It also prepares you for the workforce and equips you with the ability to keep learning as the field evolves.

If you’re ready to take your data science skills to the next level, consider joining our BlackBelt AI/ML Program. It’s designed to boost your expertise and empower you to excel in challenging data science projects. Your future in data science begins here. Don’t miss out on this opportunity to advance your career. Join us now!

Frequently Asked Questions

Q1. What resources are needed to become a data scientist?

A. To become a data scientist, you’ll need resources like relevant courses, books, online tutorials, access to data, programming languages like Python or R, and analytical tools.

Q2. What are the 4 types of data science?

A. A: The four types of data science are descriptive (summarizing data), diagnostic (explaining data patterns), predictive (forecasting trends), and prescriptive (providing recommendations).

Q3. Can I self-study data science?

A. Yes, you can self-study data science through online courses, tutorials, books, and practice with real-world datasets. Many resources are available for self-learners.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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