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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

Top 5 Data Analytics Certifications

Yana Khare
Yana Khare Last Updated : 06 Oct, 2023
6 min read

Introduction

In our era of data supremacy, the ability to decipher the language of numbers and extract meaningful insights has never been more valuable. Data analytics has transcended being just a buzzword; it has become an indispensable tool across industries, steering businesses toward success and innovation. A Data Analytics Certification is your compass to stand out in this dynamic landscape and embark on a rewarding career. But with a plethora of options available, how do you choose the right one? Let’s embark on a journey to explore the top 5 Data Analytics Certifications that are shaping the data analytics landscape.

Table of contents

Why Should One Opt For Data Analytics Certification?

The demand for skilled data analysts is soaring in today’s data-driven world. Data analytics has become a critical component of decision-making across industries, significantly increasing the importance of certifications in this field. But why should you consider pursuing a Data Analytics Certification? Here are some compelling reasons:

  1. Career Advancement: A Data Analytics Certification can be a powerful catalyst for career growth. It demonstrates your expertise and commitment to potential employers, making you a valuable asset in a competitive job market. Many organizations prefer certified professionals for critical data-related roles.
  2. Salary Increase: Data analysts with certifications tend to earn higher salaries than those without. According to various sources, certified data analysts often command higher paychecks. The average base pay for certified data analysts in India is ₹6,00,000, while certified senior data analysts can earn around $73,000 in the US.
  3. Industry Demand: Organizations rely on data-driven insights to make informed decisions, which requires skilled data professionals. Obtaining a certification demonstrates your proficiency and positions you as a sought-after candidate for the job.
  4. Increase in Job Roles: Data analytics is no longer limited to a single role. Certified professionals often find themselves eligible for various job roles, including senior data analyst, data scientist, data analytics manager, and business analyst. Certifications broaden your career prospects and open doors to diverse opportunities.
  5. Industry Recognition: Reputable certifications are recognized and respected within the industry. They validate your skills and knowledge, earning you the trust of colleagues, employers, and clients. Being certified can enhance your professional reputation.
  6. Skill Enhancement: Data Analytics Certifications involve rigorous training and assessments, allowing you to acquire new skills and expand your knowledge. You’ll gain expertise in data analysis, visualization, and interpretation, making you more proficient in handling complex data challenges.

Top 5 Data Analytics Certification

Let’s look at the curriculum, duration and other details of top 5 data analytics certificates:

Certified Business Analytics Program

Certified Business Analytics Program | Top 5 Data Analytics Certifications

The Certified Business Analytics Program offered by Analytics Vidhya is a comprehensive course designed to equip individuals with the skills and knowledge needed for a career in business analytics. The program is aimed at professionals and beginners in business analytics. It covers various tools and techniques commonly used in the industry, making it suitable for newcomers and those looking to enhance their skills. The course offers 1:1 mentorship with practitioners, a comprehensive and personalized learning path, and prepares individuals for interviews.

The program covers a wide range of topics, such as:

  • Microsoft Excel
  • MySQL for Business Analytics
  • Mastering Python
  • Data Exploration and Statistical Inference
  • Storytelling and Dashboarding using Tableau
  • Storytelling and Dashboarding using PowerBi
  • Problem Formulation and Communication
  • Basic Machine Learning
  • Time Series

Start learning now: Certified Business Analytics Program

Introduction to Python

Introduction to Python

Analytics Vidhya’s “Introduction to Python” course is a foundational program designed to provide learners with a comprehensive understanding of using Python in the field of data science. This course serves as a stepping stone for individuals interested in embarking on a data science journey or who want to solidify their fundamentals in Python programming. “Introduction to Python” is a free course that will help an individual learn Python libraries like Pandas and use them efficiently for data science and data analysis.

The program covers a wide range of topics, such as:

  • Introduction to Python
  • Understanding Operators in Python
  • Variables and Data Types in Python
  • Understanding and Implementing Conditional Statements in Python
  • Understanding Standard Libraries in Python
  • Reading a CSV File in Python
  • Data Frames and Basic Operations with Data Frames
  • Indexing a Data Frame
  • Data Manipulation and Visualisation

Start learning now: Introduction to Python

Certified AI & ML BlackBelt Plus Program

Certified AI & ML BlackBelt Plus Program | Top 5 Data Analytics Certifications

Analytics Vidhya’s BlackBelt Plus is a comprehensive program designed to equip learners with the knowledge and skills required to excel in the field of data science. This course is ideal for individuals aspiring to become data scientists or enhance their expertise. This course offers weekly mentorship calls, a dedicated career acceleration team, upskilling in Deep Learning, Computer Vision, NLP, and more. An individual gets to work on 50+ real-world projects with experts.

The program covers a wide range of topics, such as:

  • Microsoft Excel
  • Create Charts and Visualizations using MS Excel
  • MySQL
  • Working with Data Frames, Lists, and Dictionary

Start learning now: Certified AI & ML BlackBelt Plus Program

Tableau For Beginners

Tableau for Beginners

The course Tableau for Beginners, offered by Analytics Vidhya, is designed to provide individuals with a comprehensive introduction to Tableau, a powerful data visualization tool. Tableau is widely used in data analytics and business intelligence to create interactive and insightful visualizations from raw data. This course is an excellent starting point for anyone looking to harness the potential of Tableau for data visualization. Whether you are a beginner exploring data visualization for the first time or a professional seeking to enhance your data analytics skills, this course offers valuable insights and practical knowledge.

The course covers a wide range of topics, such as:

  • What is Data Visualization and Why Should We Use it
  • Navigating the Tableau Interface
  • Different Types of Charts in Tableau

Start Learning here: Tableau for Beginners

Introduction to Business Analytics

Introduction to Business Analytics | Top 5 Data Analytics Certifications

“Introduction to Business Analytics” offered by Analytics Vidhya, is a foundational program designed to introduce learners to the world of analytics. Analytics plays a crucial role in today’s data-driven business landscape, and this course is the perfect starting point for those looking to develop their analytical skills. Whether you are a newcomer to the field or someone seeking to reinforce your understanding of analytics, this course provides a structured learning path.

The course covers a wide range of topics, such as:

  • What is Business Analytics?
  • Data Scientist vs. Data Engineer vs. Business Analyst
  • Sample Problems and Projects – Business Analytics vs. Data Science
  • Terms Related to Business Analytics
  • Management Information Systems (MIS)
  • Detective Analysis
  • Business Intelligence
  • Predictive Modeling
  • Artificial Intelligence and Machine Learning
  • Skills Required in Business Analytics Roles

Start Learning here: Introduction to Business Analytics

Conclusion

As we conclude this exploration of the top 5 Data Analytics Certifications, remember that continuous learning and skill development are paramount in the data-driven world. By embarking on a certification journey, you’re investing in yourself and aligning with the future of decision-making, innovation, and success, one data point at a time. So, choose your path, embark on your journey, and let data analytics open doors to a brighter professional future.

Frequently Asked Questions

Q1. What are data analytics courses?

A. Data analytics courses are educational programs that teach the skills and techniques needed to analyze and interpret data for decision-making. These courses cover data collection, processing, visualization, and statistical analysis.

Q2. Is data analytics in demand?

A. Yes, data analytics is in high demand across industries. Organizations rely on data analysts to extract valuable insights from data, leading to informed decisions and improved business outcomes.

Q3. Is data analyst a high-paying job?

A. Data analysts can earn competitive salaries. The pay varies based on factors like experience, location, and the industry.

Q4. Who is eligible for the data analyst course?

A. Data analyst courses are open to individuals with varying educational backgrounds. Typically, a bachelor’s degree in a related field like mathematics, statistics, or computer science is preferred, but what matters is a passion for data.

Q5. How do I start a career in data analytics?

A. To start a career in data analytics, follow these steps:
1. Acquire relevant skills through courses or self-study.
2. Build a strong foundation in statistics and data manipulation.
3. Gain practical experience through internships or projects.
4. Create a portfolio showcasing your work.
5. Apply for entry-level data analyst positions.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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