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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

5 Free Data Analytics Courses to Kickstart Your Career

Ayushi Trivedi
Ayushi Trivedi Last Updated : 13 Aug, 2024
5 min read

Introduction

Imagine leading the charge on the data revolution, where your insights influence important business choices and spur expansion. You may believe that starting a career in this interesting field will cost a significant sum of money, but what if you could do it for nothing? This article will show you five excellent free data analytics courses that will offer you understanding of data analytics. These courses are a great place to start if you’re a professional looking to expand your knowledge. Or an inquisitive newcomer wanting to learn more about this exciting industry. Take a look and see how you can use statistics to make significant decisions and change your career while staying within your budget.

5 Free Data Analytics Courses

Overview

  • Recognize the basic ideas and methods of data analytics.
  • Learn how to use common data analytics applications and tools with ease.
  • Acquire proficiency in data interpretation and visualization.
  • Gain the capacity to make decisions based on data.
  • Gain useful skills via practical projects and real-world implementations.

Table of contents

5 Free Data Analytics Courses to Kickstart Your Career

Let us now explore free data analytics courses which will help you in starting your career in data analysis.

1. Intro to Data Analysis (Udacity)

Udacity’s “Introduction to Data Analysis” course takes students on a thorough exploration of data analysis with an emphasis on questions, data formats, patterns, intuition, findings, and forecasts. The course culminates with a practical project utilizing Python modules such as NumPy, Pandas, and Matplotlib, with an emphasis on developing practical skills. This course equips students with the fundamental abilities needed to succeed in data analysis and is a part of the Data Analyst Nanodegree program.

Intro to Data Analysis – Udacity

Click here to access the course.

2. Introduction to Data Science in Python (Coursera)

In the Introduction to Data Science in Python course, you’ll dive into essential data science techniques and tools using Python. You’ll start by mastering advanced functions like lambdas and how to manipulate CSV files effectively. The course will guide you through common Python functionalities and features crucial for data science, including how to query and process DataFrame structures for data cleaning. Additionally, you’ll gain a solid understanding of statistical concepts such as distributions, sampling, and performing t-tests. Rated 4.5 out of 5 by over 26,961 learners, this intermediate-level course requires some prior experience and spans approximately 34 hours. With a flexible schedule, you can learn at your own pace and build a strong foundation in data science.

Free Data Analytics Courses

Click here to access the course.

3. Introduction to Business Analytics (Analytics Vidhya)

The expanding significance of business analytics in contemporary enterprises is examined in the “Introduction to Business Analytics” course. There is a growing need for qualified analysts as digitization advances. The foundations of business analytics, its uses, and its widespread acceptance across industries are all covered in the course. It gives students the know-how to comprehend how businesses apply data science solutions to enhance everyday operations and have a big impact on the industry.

Introduction to Business Analytics (Analytics Vidhya)

Click here to access the course.

4. Data Analysis with Python

Santiago Basulto’s course, “Data Analysis with Python,” provides a thorough walkthrough of the whole data analysis procedure. You will acquire the ability to read data from a variety of sources, including Excel spreadsheets, SQL databases, and CSV files. You’ll process and work with this data using robust libraries like NumPy and Pandas, and then use Matplotlib and Seaborn to show your results. Along with a comprehensive Jupyter Notebook instruction to guarantee you have a reliable workspace for your data analysis assignments, the course also contains a brief Python reference guide to help you brush up on your programming abilities.

Classically, data analysis was done using costly and company-specific solutions that may have included Tableau. However, open liberal such as Python and SQL have brought a great change in the field. By enrolling for this certification program, you will learn the rudiments of data analysis using Python and these contemporary tools. By the end of this course, you will be able to read data from different sources, process them and generate important visualizations using Python libraries hence exposing you to the real world analysis and decision making based on data.

Free Data Analytics Courses

Click here to access the course.

5. IBM Data Analyst Professional Certificate

In the IBM Data Analyst Professional Certificate course you will learn modern data analysts’ tools and skills utilized in their day-to-day practice. From this course, one will be in a position to create visualizations of the data and how one should present the findings in an Excel and BI tools such as IBM Cognos Analytics and Tableau. The course also includes efficiency in the use of Python for data analysis, use of Pandas and Numpy libraries, and use of APIs and Web Services.

You will acquire technical expertise through practical laboratories and projects and develop a portfolio to present your work. This extensive 11-course series is intended for novices and offers a professional accreditation to show off your knowledge, preparing you for a future in data analytics.

Free Data Analytics Courses

Click here to access the course.

Conclusion

Breaking the ice and investing in data analytics is not as expensive like people tend to think. These free Data Analytics Courses enable you learn with necessary knowledge and skills without having to using your pocket money. These tools offer a great chance to work on the foundations of data analysis: starting from Analytics up to working with Python or Excel. The principles you will learn and the skills you will gain while solving such courses will help you to build a successful career in this vibrant industry. Why then wait? Starting your education today opens you to the reality of the strength that comes with data in shaping your career progress.

Frequently Asked Questions

Q1. Do I need any prior experience to take these courses?

A. The majority of these courses don’t require any prior expertise because they are intended for novices.

Q2. How long do these courses take to complete?

A. Course durations vary, but most can be completed in a few weeks to a couple of months, depending on your pace.

Q3. Are these courses really free?

A. Yes, all the courses listed here are free to access. Some platforms may offer optional paid certificates.

Q4. Can I get a certificate upon completion?

A. Some courses offer free certificates, while others may charge a fee for certification.

Q5. Will these courses help me get a job in data analytics?

A. These courses provide foundational knowledge and skills that are highly valued in the field. Coupled with practical experience, they can significantly enhance your employability.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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