Module 1: Analytics - General
- What is Analytics (BI, BA, Levels, etc)
- Why Analytics (Appl in various domains..)
- Different Roles in Analytics
- Tools and Techniques in Analytics
- Data Science, Data Mining, Statistics, Machine Learning, Supervised and Non-Supervised Techniques
- CRISP Modeling Framework
- Scales of Measurements
- Quiz Module 1
Module 2: R Programming
- R Environment and R Studio
- Data Management
- Improving functionality with additional packages
- Quiz Module 2
Module 3: Basic Programming and Data Structures
- Basic Data Structures & Programming Constructs
- Libraries
- Numpy
- Pandas
- Matplotlib
- Quiz Module 3
Module 4: Data Manipulation and Descriptive Summary
- Group Summaries
- Crosstab, Pivot and Reshape data
- Managing Missing Values
- Outliers Detection
- Various types of Joins, merge
- Managing indexes in pandas
- Partitioning data into train and test set
- Scaling of Data (useful for Clustering)
- Quiz Module 4
Module 5: Statistics
- Basic Statistics (mean, median, mode)
- Other Statistics (sd, var, quantile, skewness, kurtosis)
- Hypothesis Tests (t-test, Chi-sq tests, etc)
- Probability Distributions (normal, binomial, etc)
- Sampling Techniques
- Quiz Module 5
Module 6: Graphical Representation of Data
- Selection of Graph
- Basic Graphs (histogram, barplot, boxplot, pie, etc)
- Libraries (matplotlib, seaborn, plotline)
- Managing plot parameters(size, title, axis, legend, etc)
- Advanced Graphs (correlation, heatmap, mosaic, etc)
- Exporting graphs
- Quiz Module 6
Module 7: Linear Regression
- Simple Linear Regression
- Multiple Linear Regression
- Libraries - sklearn, statsmodel
- Predict DV on IVs
- Metrics of Linear Regression(R2, RMSE, p-values)
- Applications of Linear Regression
- Assumptions of Linear Regression
- Quiz Module 7
Module 8: Logistic Regression
- Difference between Linear and Logistic
- Logistic Regression
- Metrics of Logistic Regression (confusion matrix, ROC curve)
- Predict the probability of DV on IV
- Applications of Logistic Regression
- Quiz Module 8
Module 9: Classification
- Difference between classification and regression decision trees from CART models
- Understanding tree from the plot
- Classification Tree - predict class, plot, accuracy
- Regression Tree - predict numerical value, plot, RMSE
- Improving tree accuracy using Random Forests
- Bagging and Boosting
- Applications of Decision Tree
- KNN (k-nearest neighbors)
- Neural Networks
- Gradient Descent
- SVM (Support Vector Machine)
- Quiz Module 9
Module 10: Cluster Analysis
- Clustering for Grouping Data
- Types - Hierarchical & Non-Hierarchical
- K Means - output metrics (iter, error, plot)
- Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot
- Extracting the data in clusters, Cluster Centers
- Applications of Clustering
- Quiz Module 10
Module 11: Association Rule Analysis
- Applying AR to the grocery store for Market Basket Analysis
- Metrics- Support, Confidence, Lift
- Frequent Itemsets and Rules; Filtering rules
- Applications of AR
- Quiz Module 11
Module 12: Text Mining
- Managing Unstructured Data; Unstructured to Structured Data
- Extracting Tweets from Twitter
- Extracting words for Sentiment Analysis
- Wordcloud to visualize the frequency of occurrence of words in the text
- Applications of Text Mining
- Quiz Module 12
Module 13: Complimentary Module 1: Soft Skills Development
- Business Communication
- Preparation for the Interview
- Presentation Skills
Module 14: Complimentary Module 2: Resume Writing
- Resume Writing

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