Foundation of Data Analytics :

Introduction ,Evolution , Concept and Scopes , Data , Big Data, Metrics and Data classification, Data Reliability & Validity, Problem Solving with Analytics, Different phases of Analytics in the business and Data science domain, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics , Different Applications of Analytics in Business, Text Analytics and Web Analytics, Skills for Business Analytics , Concepts of Data Science, Basic skills required for understanding Data Science.

 Python Programming :

Introduction to Python Editors & IDE’s (Jupyter, Spyder, pycharm, etc...), custom environment settings, basic data types -numeric, string, float, tuples, list ,dictionary ,sets and their operations, control flow (if-elif-else), loops (for, while), inbuilt functions for data conversion, writing user defined functions.

 Concepts of packages/libraries :

Important packages like NumPy, SciPy, scikit-learn, Pandas, Matplotlib, seaborn, etc., installing and loading packages, reading and writing data from/to different formats, tuples, sets, dictionaries, simple plotting, functions, list comprehensions, database connectivity.

 Descriptive Analytics :

Describing and summarizing data sets, measures of central tendency, dispersion, skewness, kurtosis, Correlation.

Probability :

Measures of probability, conditional probability, independent event, Bayes’ theorem, random variable, discrete (binomial, Poisson, geometric, hypergeometric, negative binomial) and continuous (uniform, exponential, normal, gamma). Expectation and variance, markov inequality, chebyshev’s inequality, central limit theorem.

 Inferential Statistics:

Sampling & Confidence Interval, Inference & Significance. Estimation and Hypothesis Testing, Goodness of fit, Test of Independence, Permutations and Randomization Test, t-test/z-test (one sample, independent, paired), ANOVA, chi-square.

 Relevance in industry, Statistical learning vs machine learning, types and phases of analytics.

 Data pre-processing and cleaning:

 data manipulation steps (sorting, filtering, duplicates, merging, appending, subsetting, derived variables, data type conversions, renaming, formatting, etc.), normalizing data, sampling, missing value treatment, outliers.

 Exploratory data analysis:

  Data visualization using matplotlib, seaborn libraries, creating graphs (bar/line/pie/boxplot/histogram, etc.), summarizing data, descriptive statistics, univariate analysis (distribution of data), bivariate analysis (cross tabs, distributions and relationships, graphical analysis).

 Introduction, Applications of Machine Learning, Key elements of Machine Learning, Supervised vs. Unsupervised Learning.

 Supervised Machine Learning :

 Linear Regression, Multiple Linear Regression Polynomial Regression.

 Classification :

 Using Logistic Regression, Logistic Regression vs. Linear Regression, Logistic Regression with one variable and with multiple variables, Application to multi-class classification. The problem of Overfitting, Application of Regularization in Linear and Logistic Regression. Regularization and Bias/Variance. Classification using K-NN, Naive Bayes classifier, Decision Trees (CHAID Analytics), Random Forest, Support Vector Machines.

 Model Evaluation:

 Cross validation types (train & test, bootstrapping, k-fold validation), parameter tuning, confusion matrices, basic evaluation metrics, precision-recall, ROC curves.

  Case study :

Neural Networks:

Introduction, Model Representation, Gradient Descent vs. Perceptron Training, Stochastic Gradient Descent, Multiclass Representation, Multilayer Perceptrons, Backpropagation Algorithm for Learning, Introduction to Deep Learning.

Association Rule Mining:

Mining frequent itemsets, Apriori algorithm, market basket analysis.

case study

Unsupervised Machine Learning:

Introduction, Clustering, K-Means algorithm, Affinity Propagation, Agglomerative Hierarchical, DBSCAN, Dimensionality Reduction using Principal Component Analysis.

case study : Application of PCA

Time Series Forecasting:

Trends and seasonality in time series data, identifying trends, seasonal patterns, first order differencing, periodicity and autocorrelation, rolling window estimations, stationarity vs. non-stationarity, ARIMA and ARIMAX Modeling

case study

Introduction to Operations Research (OR), Linear Programming Problems (LPP), Geometry of linear programming, Sensitivity and Post-optimal analysis, Duality and its economic interpretation.

Network models and project planning, Non-linear Programming – KKT conditions, Dynamic Programming.