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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

Free Course on Business Analytics

Aayush Tyagi
Aayush Tyagi Last Updated : 22 Apr, 2024
5 min read

Introduction

In the data-driven world of today, understanding business analytics is like having a supermachine in your professional toolbox. It involves more than just crunching numbers — it involves uncovering insights that can change the game and propel success. That’s why business analysts are in high demand. But the question is “How to become a business analyst” without spending money. We have your answer. In this article we will explore a free course on business analytics by Analytics Vidhya. 

Let’s start!

free course on business analytics and analysis

Table of contents

Understanding Business Analytics

The primary focus of business analytics is on leveraging data availability to make better-informed decisions. This is a valuable addition to your business, enabling you to focus on what really matters.

This encompasses several elements, such as data analysis, through which patterns and trends that are present in the data may be identified. The data visualization process then begins, helping to translate these insights into understandable charts and graphics.

Plus, predictive modeling lets you peek into the future, while business intelligence ties it all together, giving you a bird’s-eye view of your organization’s performance.

Importance of Business Analytics

  • Enhancing Decision-Making Processes: Imagine having a crystal ball that tells you which path to take. That’s the power of business analytics. By providing actionable insights, it guides strategic decisions, helping businesses steer in the right direction.
  • Gaining Competitive Advantage: In today’s cutthroat business landscape, staying ahead of the curve is crucial. Business analytics gives companies an edge by uncovering hidden opportunities and spotting potential pitfalls before they arise.
  • Improving Operational Efficiency: Efficiency is the name of the game in business. By optimizing processes and cutting unnecessary costs, analytics acts as a turbocharger, helping businesses run smoother and faster.
  • Meeting Customer Needs: Customers are the lifeblood of any business. With analytics, companies can tune into customer preferences like never before, delivering personalized experiences that keep them coming back for more.

Also Read: Top 8 Business Analyst Certifications

Benefits of Becoming a Business Analyst

  • Strong Demand: Businesses are desperate for seasoned analysts who can find the cause of the data gaps. The need for experts who can translate abstract numbers into concrete insights is growing, ranging from finance to health care.
  • Profitable Career Perspective: Show me the money! Business analysts benefit from competitive pay and a variety of career opportunities. This is a path to success rather than just a job.
  • Dynamic Work Environment: Business analysts will all be smitten by the diversity that is the taste of life. The possibilities are endless, whether one is calculating numbers for a tech giant or optimizing supply chains for a small retailer.
  • Possibilities for Innovation: Innovation is the lifeblood of progress, and business analysts are at the forefront of this. By utilizing insights based on data, they enhance creativity, drive forward changes in organizations, and shape the future of the company.

Introduction to Business Analytics by Analytics Vidhya

Introduction to Business Analytics by Analytics Vidhya

Analytics Vidhya’s “Introduction to Business Analytics” is a free course designed for beginners with no prior data analysis experience. The free course aims to equip you with the fundamental knowledge and skills to navigate the world of business analysis. Here’s a breakdown of what you can expect:

  • Understanding Business Analytics: The course dives into the core concepts of business analytics. You’ll learn what it is, why it’s crucial for businesses, and how it’s used to make data-driven decisions.
  • Essential Business Analytics Terminology: The course equips you with the necessary vocabulary used in business analytics. You’ll gain an understanding of key terms like Management Information Systems (MIS), detective analysis, and more.
  • Skills Required for Business Analytics: This section outlines the skillset needed to thrive in business analytics roles. It may cover areas like data analysis, statistical modeling, and communication skills.
  • Roadmap to a Business Analytics Career: The course might provide a roadmap to guide your journey in business analytics. This could include suggested learning paths, resources, or even information on relevant certifications.
  • Hands-on Learning (Possible): While it’s a free course, there’s a chance it may include practical exercises or case studies to solidify your understanding of the concepts.

Explore our Introduction to Business Analytics Course here!

Course Outline

1. What is Business Analytics?

  • Understanding the fundamentals of Business Analytics.
  • Quiz: Test your knowledge on the basics of Business Analytics.
  • Joining an exciting startup: Exploring the role of Business Analytics in startup environments.
  • Quiz: Differentiating between Data Scientist, Data Engineer, and Business Analyst roles.
  • Sample problems and projects: Contrasting Business Analytics and Data Science scenarios.
  • Quiz: Assessing your understanding of sample problems and projects in Business Analytics and Data Science.
  • Exploring the differences between Business Analytics and Data Science.
  • Career prospects in Business Analytics.

2. Spectrum of Business Analytics**

  • Key terms related to Business Analytics.
  • Overview of Management Information Systems (MIS).
  • Understanding Detective Analysis.
  • Exploring Business Intelligence tools.
  • Introduction to Predictive Modeling.
  • Insights into Artificial Intelligence and Machine Learning in Business Analytics.
  • Typical problems tackled by Business Analysts.

3. Skills Required for Business Analytics and Roadmap of Business Analytics Program**

  • Essential skills for Business Analytics roles.
  • Downloading the roadmap for Certified Business Analytics Program (CBAP).
  • Bonus Section: Logistics of Certified Business Analytics Program from Analytics Vidhya.

4. Case Study: Ezine Publishing**

  • Overview of the Ezine Publishing case study.
  • Understanding the business dynamics.
  • Quiz: Identifying focus categories for Ezine Publishing.
  • Analyzing granular traffic data.
  • Identifying effective marketing channels.
  • Maximizing revenue streams.
  • Targeting specific customer segments.
  • Analyzing traffic distribution patterns.
  • Optimizing advertisements for Ezine Publishing.
  • Where to proceed next for further learning in Business Analytics.

About the instructor

Founder and CEO of Analytics Vidhya: Kunal Jain is the founder and CEO of Analytics Vidhya, the platform you’re taking the course on. This indicates his deep commitment to the field of business analytics and data science education. With a background in Aerospace Engineering from IIT Bombay and over 10 years of experience in Global Business Analytics, Kunal Jain brings rich industry knowledge to the course. Having built a successful platform like Analytics Vidhya suggests a strong passion for sharing knowledge and fostering a data-driven community.

Conclusion

Diving into the world of business analytics offers a good journey and awesome rewards, and Analytics Vidhya’s free course on business analysis is the perfect starting point. From understanding the basics to case studies, this course covers it all, ensuring you’re equipped to make impactful data-driven decisions.

Led by industry expert Kunal Jain, the course provides not only knowledge but also practical skills through quizzes and case studies. It’s a journey towards a lucrative career, guided by someone passionate about empowering learners like you.

So, why wait?

Start your business analytics journey today with Analytics Vidhya and unlock a world of possibilities!

Aayush Tyagi
Aayush Tyagi

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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