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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

8 In-Demand Data Science Certifications for Career Advancement [2024]

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Analytics Vidhya Last Updated : 04 Mar, 2024
6 min read

The job opportunities for data scientists will grow by 36% between 2021 and 2031, as suggested by BLS. It has become one of the most demanding job profiles of the current era. As recruiters hunt for professionals who are knowledgeable about data science, the average median pay for a proficient Data Scientist has soared to $100,910 annually. With a Data Science Course certifications, you stand a stronger chance to contest in the competitive world and help brands improve their administration of various services, customer experience, advertising campaigns, and business initiatives.

data science certifications

Table of contents

What is Data Science?

Data science is a branch of applied mathematical and statistical analysis that generates important data from massive quantities of large datasets, commonly known as big data. It is also known as a data-driven scientific method. 

Data science uses techniques from various fields to collect data sets, analyze them, draw conclusions, and analyze the results to use in evaluation. It is a multidisciplinary study of mining, statistics, machine learning, analytics, and coding.

stages of data science lifecycle - data science certifications
Source: Berkeley.edu

Why Opt for Data Science Certifications?

 Data scientists are in high demand, but the talent pool is often highly competitive. Many people are doing everything they can to get certified in the data science discipline and set themselves apart from competitors since this would also be a successful career path to explore. Some of the benefits of data science courses are as follows:

  • Recognition: When businesses are looking to recruit a data scientist, applicants with a data science certification from a recognized online platform are always prioritized. Suppose a person can present that they have completed a professional data science certification program from a good institution. This is a sign of skilled training, practical knowledge, and excellent academic understanding.
  • Updated Knowledge: By enrolling in data science courses, you’ll be able to be updated with the most current trends in the industry. The course would allow an individual to grasp various new facts associated with how the field of data science is developing and gain insights about the latest innovations.
  • Skill Cultivation: A comprehensive data science certification online will educate the students about the relevant information and skills required to execute the job duties and complete the assigned tasks in a data science career, such as that of a data scientist.
  • Clarity of Concepts: A reputable online institution’s professional certification program helps every student gets individualized attention from every faculty, along with interactive doubt sessions, live classes, individually scored papers, and final projects.

What Will You Learn in Data Science Courses?

With data science courses from a well-known institution, an individual can effectively learn the following fundamental skills:

  • Data science includes basic technical skills such as data mining, big data, artificial intelligence, etc.
  • Certain management skills include communication, data analysis, and project management.
  • Essential mathematical skills, including statistics, ML, algorithms, and others
  • C, JAVA, Python, etc., types of programming languages are also incorporated when the course curriculum is designed.
Skills learned in a data science course - Data Science program
Source: Berkeley.edu

Top 8 Data Science Courses and Certifications 

Here are the top 8 Data Science Programs offered by Analytics Vidhya. There courses are designed keeping in mind the need of learning new data skills to stay relevant at your workplace.

#1.BlackBelt Plus

Analytics Vidhya designs the BlackBelt Program for those who want to become full-stack data scientists. The system is among the best data science courses. Some of the unique characteristics of this course are as follows:

  • Comprehensive course design with more than 500 hours of learning
  • Live one-on-one classes with the industry experts
  • More than 50 real-life related projects
  • Technical mentor sessions
  • Mock interviews with career acceleration teams
  • 100% placements
  • Personalized road maps for learning and professional development

Cost: ₹64,999
Explore our BlackBelt Plus Program here!

#2. Data Science Bootcamp 

This is one of the best data science boot camp online courses. It is created by experts for individuals who want to excel in data science. The course includes the following features which could help data enthusiasts to get their desired careers:

  • Well-designed course plan with flexible learning modules
  • Personalized one-on-one mentor sessions with the industry experts
  • Training for more than 12 tools
  • Multiple doubt sessions
  • Hands-on experience on more than 20 industry-level projects
  • Job guarantee in the leading organizations

Cost: ₹59,999
Explore our Data Science Bootcamp here!

#3. Applied Machine Learning – Beginner to Professional

True to its name, this online data science training course takes you on a complete journey and helps you advance from beginner to professional level. The course is ideal for all who plan to brush up on their fundamental concepts of ML. It is also one of the data science certifications best suited for learning the tools and techniques generally used in machine learning to strengthen businesses’ marketing campaigns, operations, etc. Some of the key features are as follows:

  • A detailed curriculum with about 577 lessons
  • 6 Hands-on industry-level projects
  • ML techniques such as K-means clustering and hierarchical clustering
  • Real-world applications of ML and data science
  • Lessons on techniques like Principal Component Analysis (PCA) and t-SNE
  • Elaborate discussions and sessions on ensemble Modeling techniques like Boosting, Kernel Tricks, Support Vector Machines (SVM), and Bagging
  • Training on tools like Keras, seaborn, Python, MLBox, etc.

Cost: ₹14,999
Explore our Applied Machine Learning – Beginner to Professional certification here!

#4. Natural Language Processing (NLP) Using Python

Learn how to extract information from unstructured data through Natural Language Processing. This course entails the basics of Python, techniques like NLP using neural networks, TF-IDF, deep learning, and more. Some of the amazing features of this data science certification are as follows:

  • A practical and comprehensive course design
  • Expert live chat for an hour daily
  • Regular quizzes and assignments
  • Learn about industry-relevant techniques such as POS tagging, NER, RNNs, etc.
  • 190+ video lessons delivered by the industry experts
  • Perform interesting NLP projects using deep learning
  • Get the method on how to perform Parts-of-Speech tagging and Name Entity Recognition

Cost: ₹10,999
Explore our Natural Language Processing (NLP) Using Python certification here!

#5. Applied Computer Vision using Deep Learning

One of the perfect data science certification designed for people inclined to learn about computer vision, deep learning frameworks and techniques. Computer vision is a leading field of interest for individuals, businesses and leading companies. The features of this course include the following:

  • A well-designed coursework
  • Video lessons by computer vision professionals
  • Learn skills such as image localization, object detection, face and pose detection, and more
  • Classes on techniques, including architectures like RCNN, fast RCNN & deep learning models like SSD, Retinanet and more
  • Industry-level projects

Cost: ₹10,999
Explore our Applied Computer Vision using Deep Learning Course here!

#6.Fundamentals of Deep Learning

A basic data science certification course provides fundamental knowledge about deep learning concepts. The course is created in such a manner assuming that the students need more knowledge of Natural Language Processing, neural networks and deep learning. The features that create the course so elaborate are as follows:

  • The course includes more than 250 lessons which the industry experts create
  • The course includes tools and techniques like Convolution and Recurrent Neural Networks, advanced sequence models like LSTM and GRU
  • Learn how to use deep learning for image detection, text and voice data
  • The course includes training on the tools such as Keras, pandas, RegEx and more
  • 5 hands-on, real-life projects

Cost: ₹7,999
Explore our Fundamentals of Deep Learning program here!

#7. Introduction to Data Science (LPU)

The ideal course to start your journey in data science. This course incorporates the fundamentals of data science and industry-relevant techniques and tools. The course helps you build a strong data science foundation and prepare for a bright data science career. Some of the features of this data science course are as follows:

  • More than 60 hours of comprehensive lessons
  • A well-structured course including an introduction to the concepts of Python, statistics, data science and predictive models
  • Live chat interaction with an expert for an hour regularly
  • 5 real-life projects to give you knowledge about the industrial concept of data science
  • Easy-to-understand modules

Cost: ₹7,999
Explore our Introduction to Data Science certification here!

#8. Introduction to Machine Learning

Machine learning is a progressively growing field of study. There is a massive demand for skilled professionals in this industry. This data science certification course is perfect for candidates with little to no data science and machine learning knowledge. The method entails a curriculum from basics to advanced learning of the concepts of machine learning and data science, preparing one for a job in the industry. Some of the features and highlights of this course are as follows:

  • It includes more than 300 video lessons which the industry professionals have delivered
  • It covers training on tools like NumPy, pandas, SciPy, statsmodels and more
  • The course has more than 60 hours of comprehensive study material.
  • Get practical experience on 5 real-life related projects
  • Be a part of the live Q&A sessions with industry experts

Cost: ₹7,999
Explore our Introduction to Machine Learning program here!

Kickstart Your Data Science Career 

Data science is a comprehensive analysis of vast quantities of information. It entails deriving scientifically valid conclusions from raw, organized, and unstructured information that uses various tools, methods, and techniques. It is an interdisciplinary field that manipulates data using tools and strategies to discover something fresh and significant. Data Science has emerged as one of the leading career choices in recent years. Analytics Vidhya is the leading online platform that provides people with the best data science ceritifications. Begin your journey as a data scientist in your ideal company and fulfil your dreams with tremendous professional growth through our data science courses. Stay updated with everything happening int he world of Data with our Blogs!

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Top 10 Data Analytics Projects with Source Codes
Top 10 Data Analytics Projects with Source Codes
The Ultimate Guide to Choosing the Best Data Science Course to Boost Your Career in 2023
The Ultimate Guide to Choosing the Best Data Science Course to Boost Your Career in 2023

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