Single Courses

Course Syllabus

Data Analytics training syllabus designed by STG, Ghaziabad is completely based on the current industrial requirement of the companies with live project work. We Provide complete data analytics from beginner to advanced level.

• Section 1: Introduction to Data Analytics

  • 1. Introduction to Data Analytics
  • 2. Brief history of Data Analytics
  • 3. What is Data Analytics?
  • 4. Why is Data Analytics important?
  • 5. What benefits does Data Analytics have?

• Section 2: Introduction to Python Basic Statistics

  • 1. High-Level overview of Data Science / Machine Learning project management methodology
  • 2. Random Variable and its definition
  • 3. Probability and Probability Distribution
  • 4. Balanced vs Imbalanced datasets
  • 5. Sampling Funnel, its application and its components
  • 6. Measure of central tendency
  • 7. Measure of Dispersion
  • 8. Expected value of probability distribution
  • 9. Measure of Skewness
  • 10. Measure of Kurtosis
  • 11. Various graphical techniques to understand data
  • 12. Installation of Python IDE
  • 13. Anaconda and Spyder
  • 14. Working with Python with some basic commands
  • 15. Normal Distribution
  • 16. Standard Normal Distribution / Z distribution
  • 17. Z scores and Z table
  • 18. QQ Plot / Quantile-Quantile plot
  • 19. Sampling Variation
  • 20. Central Limit Theorem
  • 21. Sample size calculator
  • 22. T-distribution / Student's-t distribution
  • 23. Confidence interval

• Section 3: Hypothesis Testing

  • 1. Parametric vs Non-parametric tests
  • 2. Formulating a Hypothesis
  • 3. Choosing Null and Alternative hypothesis
  • 4. Type I and Type II errors
  • 5. Comparative study of sample proportions
  • 6. ANOVA
  • 7. 2 Proportion test
  • 8. Chi-Square test
  • 9. Non-Parametric test
  • 10. Non-Parametric test continued
  • 11. Hypothesis testing using Python

• Section 4: Linear Regression

  • 1. Scatter Diagram
  • 2. Correlation Analysis
  • 3. Principles of Regression
  • 4. Introduction to Simple Linear Regression
  • 5. R shiny and Python Flask
  • 6. Multiple Linear Regression
  • 7. Scatter diagram
  • 8. Ordinary least squares
  • 9. Principles of regression
  • 10. Splitting the data into training, validation and testing datasets
  • 11. Understanding Overfitting vs Underfitting
  • 12. Generalization error and Regularization techniques
  • 13. Introduction to Simple Linear Regression
  • 14. Heteroscedasticity / Equal Variance
  • 15. LINE assumption
  • 16. Multiple Linear Regression
  • 17. Model Quality metrics
  • 18. Deletion diagnostics

• Section 5: Logistic Regression/ Regularization Techniques/ multinomial Regression

  • 1. Principles of Logistic Regression
  • 2. Types of Logistic Regression
  • 3. Assumption and Steps in Logistic Regression
  • 4. Analysis of Simple Logistic Regression result
  • 5. Multiple Logistic Regression
  • 6. Confusion matrix
  • 7. Receiver operating characteristics curve (ROC curve)
  • 8. Lift charts and Gain charts
  • 9. Lasso and Ridge Regressions
  • 10. Logit and Log Likelihood
  • 11. Category Baselining
  • 12. Modeling Nominal categorical data

• Section 6: Data Mining Unsupervised-Clustering/ Dimension Reduction/ Data Mining Unsupervised-Network Analytics

  • 1. Supervised vs Unsupervised learning
  • 2. Data Mining Process
  • 3. Measure of distance
  • 4. Types of Linkages
  • 5. Hierarchical Clustering / Agglomerative Clustering
  • 6. Non-clustering
  • 7. Why dimension reduction
  • 8. Advantages of PCA
  • 9. Calculation of PCA weights
  • 10. 2D Visualization using Principal components
  • 11. Basics of Matrix algebra
  • 12. SVD – Decomposition of matrix data
  • 13. Definition of a network (the LinkedIn analogy)
  • 14. Measure of Node strength in a Network
  • 15. Introduction to Google Page Ranking

• Section 7: Data Mining Unsupervised-Association Rules/ Recommender System

  • 1. What is Market Basket / Affinity Analysis
  • 2. Measure of association
  • 3. Apriori Algorithm
  • 4. Sequential Pattern Mining
  • 5. User-based collaborative filtering
  • 6. Measure of distance / similarity between users
  • 7. Driver for recommendation
  • 8. Computation reduction techniques
  • 9. Search based methods / Item to item collaborative filtering
  • 10. SVD in recommendation
  • 11. Vulnerability of recommender systems

• Section 8: Text Mining

  • 1. Sources of data
  • 2. Bag of words
  • 3. Pre-processing, corpus DTM and TDM
  • 4. Word Clouds
  • 5. Corpus level word clouds
  • 6. Semantic network
  • 7. Clustering
  • 8. Extract Tweets from Twitter
  • 9. Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
  • 10. Install Libraries from Shell
  • 11. Extraction and text analytics in Python

• Section 9: Natural Language Processing/ Machine Learning Clasifiers- KNN/ Classifier-Naive Bayes

  • 1. LDA
  • 2. Topic Modeling
  • 3. Sentiment Extraction
  • 4. Lexicons and Emotion Mining
  • 5. Deciding the K value
  • 6. Building a KNN model by splitting the data
  • 7. Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
  • 8. Probability – Recap
  • 9. Bayes Rule
  • 10. Naive Bayes Classifier
  • 11. Text Classification using Naive Bayes

• Section 10: Decision Tree and Random Forest/ Bagging and Boosting/ Black Box Methods

  • 1. Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
  • 2. Greedy algorithm
  • 3. Measure of Entropy
  • 4. Attribute selection using Information Gain
  • 5. Ensemble techniques
  • 6. Decision Tree C5.0 and understanding various arguments
  • 7. Random Forest and understanding various arguments
  • 8. Boosting / Bootstrap Aggregating
  • 9. AdaBoost / Adaptive Boosting
  • 10. Stacking
  • 11. Gradient Boosting
  • 12. Extreme Gradient Boosting (XGB)
  • 13. Artificial Neural Network
  • 14. Biological Neuron vs Artificial Neuron
  • 15. ANN structure
  • 16. Activation function
  • 17. Network Topology
  • 18. Support Vector Machines
  • 19. Classification Hyperplanes
  • 20. Best fit “boundary”
  • 21. Kernel Trick

• Section 11: Forecasting

  • 1. Introduction to time series data
  • 2. Steps of forecasting
  • 3. Components of time series data
  • 4. Scatter plot and Time Plot
  • 5. Lag Plot
  • 6. ACF - Auto-Correlation Function / Correlogram
  • 7. Visualization principles
  • 8. Naive forecast methods
  • 9. Errors in forecast and its metrics
  • 10. Model Based approaches
  • 11. Model-Based approaches
  • 12. AR (Auto-Regressive) model for errors
  • 13. Random walk
  • 14. ARMA (Auto-Regressive Moving Average), Order p and q
  • 15. ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
  • 16. Data-driven approach to forecasting
  • 17. Smoothing techniques
  • 18. De-seasoning and de-trending
  • 19. Econometric Models
  • 20. Forecasting Best Practices
  • 21. Forecasting using Python

• Section 12: Project/ INTERVIEW PREPARATION

  • 1. Covering all the concepts
  • 2. Project Work
  • 3. Resume Preparation
  • 4. Interview Question Preparation