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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

Elevate Your Skills with Fractal Data Science Professional Certificate

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Analytics Vidhya Last Updated : 29 Sep, 2023
3 min read

In today’s data-driven world, skilled data scientists are in high demand. According to the US Bureau of Labor Statistics, data science is projected to grow 35% between 2021 and 2032. This surge translates to an estimated 11.5 million new job openings by 2026, making data science an exceptionally promising career choice.

Analytics Vidhya is excited to announce a game-changing addition to our educational offerings. We have partnered with Fractal, a leading analytics and AI solutions player, to bring you the Fractal Data Science Professional Certificate. This comprehensive program is available on Coursera. 

The Fractal Data Science Professional Certificate is for anyone aiming to succeed in the field of data science. Whether you’re a recent graduate starting your career or a seasoned professional looking to enhance your skills, our professional certificate is tailored to provide you with the vital skills required in the fast-growing data science industry.

To excel in this program, you don’t necessarily need a prior understanding of programming concepts, but it can be helpful. What truly counts is a curious mind and the dedication to learning new things. Along with that, an analytical mindset, a strong desire to make data-driven decisions, and meticulous attention to detail are the qualities that will help you sail through the specialization.

What Will You Learn from Fractal Data Science Professional Certificate?

Instead of focusing only on tools and applications, this program provides a holistic approach to learning data science. These are the essential skills that you will acquire: 

  1. Master Problem-Solving: Develop the ability to tackle complex data-related problems with a structured and analytical approach.
  2. Proficiency in Python, SQL, and PowerBI: Acquire the skills to use Python, SQL, and PowerBI for data analysis, manipulation, and visualization.
  3. Advanced Algorithmic Proficiency: Harness the potential of advanced algorithms, including machine learning and data mining, to extract valuable insights from data.
  4. Data-Driven Actionable Insights: Learned how to derive actionable insights from data, enabling data-driven decision-making and strategy formulation.
  5. Data Storytelling: Gain proficiency in data storytelling, enabling you to communicate data insights effectively to various stakeholders and shape data-driven strategies.
  6. Critical Data Analysis: Develop the ability to critically analyze data, identify patterns, trends, and anomalies, and make informed recommendations.
  7. Professional Distinction: Achieve professional distinction in data analysis, positioning yourself for career growth and advancement.

Curriculum 

Spread over eight courses, the Fractal Data Science Professional Certificate covers the key techniques and skills required to succeed as a data science professional. With a problem-first approach, these courses enable you to navigate the complex world of data science. 

Fractal Data Science Professional Certificate

Ready to Kickstart Your Data Science Journey? 

As the demand for skilled data scientists continues to surge, the Fractal Data Science Professional Certificate is your gateway to acquiring essential skills and knowledge in this burgeoning field. Seize this opportunity to advance your career, and enroll now!

Frequently Asked Questions 

Q1. Will I receive any certification in the completion of the program?

A. Absolutely! You will earn a certificate, providing you with the qualifications to kickstart your Data Science career across various industries.

Q2. Are the courses self-paced? Are there any fixed deadlines for assignments and assessments?

A. Yes, the courses are self-paced, allowing you to adjust deadlines according to Coursera policies. You can conveniently access lectures, readings, and assignments online, at your own pace, and from anywhere via the web or your mobile device.

Q3. Do I need to take the course in some specific order?

A. We highly recommend following the suggested course order, as the content progressively builds upon information from earlier lessons.

A
Analytics Vidhya

Analytics Vidhya Content team

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