This module will guide the candidate with the knowledge of Analytics. Learn about the different roles in Analytics. Know about the tools and techniques in Analytics. Gain knowledge about Data Science, Data Mining, Statistics, machine learning, and more. Learn about the CRISP Modeling Framework.

• 1.1 What is Analytics (BI, BA, Levels, etc)
• 1.2 Why Analytics (Appl in various domains
• 1.3 Different Roles in Analytics
• 1.4 Tools and Techniques in Analytics
• 1.5 Data Science, Data Mining, Statistics, Machine Learning, Su
• 1.6 CRISP Modeling Framework
• 1.7 Scales of Measurements

This module will help the candidate to gain knowledge about the Python Environment. Learn about Anaconda setup and various IDEs, GIT, and more. Create and Manage Analytics/ML Projects

• 2.1 Anaconda – Download & Setup
• 2.2 IDEs – Jupyter, Spyder, PyCharm
• 2.3 Git – Setup and Configuration with IDEs
• 2.4 Creating and Managing Analytics/ ML Projects

This module will help the candidate with knowledge of basic programming and data structures. Gain extensive knowledge about Libraries, NumPy, pandas, Matplotib

• 3.1 Basic Data Structures & Programming Constructs
• 3.2 Libraries
• 3.3 Numpy
• 3.4 Pandas
• 3.5 Matplotlib

This module will guide the candidate with the knowledge of Data Processing, Data Manipulation, and Descriptive summary. Know about Group summaries, crosstab, pivot, reshape data and manage missing values. Learn to manage indexes in Pandas, Scaling of data, and more

• 4.1 Pre Processing Data
• 4.2 Group Summaries
• 4.3 Crosstab, Pivot and Reshape data
• 4.4 Managing Missing Values
• 4.5 Outliers Detection
• 4.6 Various types of Joins, merge
• 4.7 Managing indexes in pandas
• 4.8 Partitioning data into train and test set
• 4.9 Scaling of Data (useful for Clustering)

This module will guide the candidate through the basics of statistics in Business Analytics. Learn extensively about Hypothesis testing, Probability distribution, and Sampling Techniques

• 5.1 Basic Statistics (mean, median, mode)
• 5.2 Other Statistics (sd, var, quantile, skewness, kurtosis)
• 5.3 Hypothesis Tests (t-test, Chi-sq tests, etc)
• 5.4 Probability Distributions (normal, binomial, etc)
• 5.5 Sampling Techniques

This module will guide you through the techniques of Graphical Representation of Data. Learn about the selection of graphs and types of graphs. Manage plot parameters and advanced graphs such as correlations, heatmap, mosaic, and more

• 6.1 Selection of Graph
• 6.2 Basic Graphs (histogram, barplot, boxplot, pie, etc)
• 6.3 Libraries (matplotlib, seaborn, plotline)
• 6.4 Managing plot parameters(size, title, axis, legend, etc)
• 6.5 Advanced Graphs (correlation, heatmap, mosaic, etc)
• 6.6 Exporting graphs

This module will guide you through the basic understanding of modeling techniques and Linear Regression. Know about multiple linear regression and its libraries. Learn the metrics of Linear Regressions and its application & assumptions

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• 7.1 Modeling Techniques
• 7.2 Simple Linear Regression
• 7.3 Multiple Linear Regression
• 7.4 Libraries – sklearn, statsmodel
• 7.5 Predict DV on IVs
• 7.6 Metrics of Linear Regression(R2, RMSE, p-values)
• 7.7 Applications of Linear Regression
• 7.8 Assumptions of Linear Regression

This module will guide the learner with knowledge of Logistic Regression. Know the metrics of logistic regression. Predict the probability of DV on IV. Know extensively about applications of Logistic regression

• 8.1 Difference between Linear and Logistic
• 8.2 Logistic Regression
• 8.3 Metrics of Logistic Regression (confusion matrix, ROC curve
• 8.4 Predict the probability of DV on IV
• 8.5 Applications of Logistic Regression

This module will guide the candidate with the knowledge of classification in Financial Analytics. Understand the tree from the plot and know about the classification tree. Learn to improve tree accuracy using random forests. Know the applications of decision tree, KNN, Neural Networks, SVM, and more

• 9.1 Difference between classification and regression decision t
• 9.2 Understanding tree from the plot
• 9.3 Classification Tree – predict class, plot, accuracy
• 9.4 Regression Tree – predict numerical value, plot, RMSE
• 9.5 Improving tree accuracy using Random Forests
• 9.6 Bagging and Boosting
• 9.7 Applications of Decision Tree
• 9.8 KNN (k-nearest neighbors)
• 9.9 Neural Networks
• 9.10 Gradient Descent
• 9.11 SVM (Support Vector Machine)

This module will guide you through the knowledge of Cluster Analysis. Know about the Clustering for grouping data and its types. Learn about extracting data in clusters and application of clustering

• 10.1 Clustering for Grouping Data
• 10.2 Types – Hierarchical & Non-Hierarchical
• 10.3 K Means – output metrics (iter, error, plot)
• 10.4 Hierarchical (Agglomerative & Divisive) – Dendrogram, Visu
• 10.5 Extracting the data in clusters, Cluster Centers
• 10.6 Applications of Clustering

This module will guide you through the knowledge of the Association Rule analysis. Learn to apply AR to the grocery store for market basket analysis. Know about the frequent Itemsets and rules and application of AR

• 11.1 Applying AR to the grocery store for Market Basket Analysi
• 11.2 Metrics- Support, Confidence, Lift
• 11.3 Frequent Itemsets and Rules; Filtering rules
• 11.4 Applications of AR

This module will guide the candidate through the understanding of Text Mining. Manage unstructured data and extract tweets from Twitter and words for sentiment analysis. Know the application of text mining

• 12.1 Managing Unstructured Data; Unstructured to Structured Dat
• 12.2 Extracting Tweets from Twitter
• 12.3 Extracting words for Sentiment Analysis
• 12.4 Wordcloud to visualize the frequency of occurrence of word
• 12.5 Applications of Text Mining