• What is Analytics (BI, BA, Levels etc.)
• Why Analytics (Application in various domains..)
• Different Roles in Analytics
• Tools and Techniques in Analytics
• Data Science, Data Mining, Statistics, Machine Learning, Supervised and Non-Supervised Techniques
• Scales of Measurement
• CRISP Modeling Framework
• Anaconda - Download & Setup
• IDEs - Jupyter, Spyder, PyCharm
• Git - Setup and Configuration with IDEs
• Creating and Managing Analytics/ ML Projects
• Basic Data Structures & Programming Constructs
• Libraries
• Numpy
• Pandas
• Matplotlib
• 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)
• Basic Statistics (mean, median, mode)
• Other Statistics (sd, var, quantile, skewness, kurtosis)
• Hypothesis Tests (t-test, Chi-square tests etc.)
• Probability Distributions (normal, binomial etc.)
• Sampling Techniques
• Selection of Graph
• Libraries (matplotlib, seaborn, plotnine)
• Basic Graphs (histogram, barplot, boxplot, pie etc.)
• Managing plot parameters(size, title, axis, legend etc.)
• Advanced Graphs (correlation, heatmap, mosaic etc.)
• Exporting graphs
• 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
• Difference between Linear and Logistic
• Logistic Regression
• Metrics of Logistic Regression (confusion matrix, ROC curve)
• Predict probability of DV on IV
• Applications of Logistic Regression
• 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 neigbours)
• Neural Networks
• Gradient Descent
• SVM (Support Vector Machine)
• Clustering for Grouping Data Types - Hierarchical & Non-Hierarchical
• Kmeans - output metrics (iter, error, plot)
• Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot
• Extracting the data in clusters, Cluster Centers
• Applications of Clustering
• Applying AR to grocery store for Market Basket Analysis
• Metrics- Support, Confidence, Lift
• Frequent Itemsets and Rules; Filtering rules
• Applications of AR
• 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 text
• Applications of Text Mining
• Creating & Managing dates and times
• Creating and managing time series data
• Download stock prices data using libraries and analyzing them
• Time Series Components(Seasonal, Irregular, Trend) & Types (Additive & Multiplicative)
• Time Series Analysis - Simple Moving Average, Exponential Smoothening, ARIMA forecasting
• Plotting Time Series, Candlesticks diagram for stocks
• Applications of Time Series Analysis
