Grade "A+" Accredited by NAAC with a CGPA of 3.46
To be updated
The course shall provide sufficient knowledge of python programming language to use for machine learning algorithm and python/R programming for statistical methods. A brief introduction of neural networks and deep learning will also be covered.
Candidates pursuing graduation, graduates, Post Graduates (must have studied mathematic at 10+2 level)
To be updated
Resource persons for this course are from the industry and eminent faculty members from reputed institutes like IIT, JNU, IIM and University of Delhi.
Account Name: Principal SSCBS Students Society A/C
Bank: State Bank of India
Account Number: 35810781108
IFS Code: SBIN0011550
MICR: 110002303
Branch: (11550)- Pascon Building Garg Trade Centre, Sector-11, Rohini, New Delhi-110085
Title: Foundation of Data Analytics & Python Programming
Duration (Hours): 20
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, simple plotting, functions, list comprehensions, database connectivity, Playing with Date Format.
Title: Probability & Statistics
Duration (Hours): 30
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.
Title: Data Munging with Python
Duration (Hours): 15
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).
Title: Machine learning – Part 1
Duration (Hours): 15
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
Title: Machine learning – Part 2
Duration (Hours): 20
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
Title: Optimization in Analytics
Duration (Hours): 15
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.
Title: Introduction to SQL and Business Intelligence
Duration (Hours): 10
Learning SQL query structure with examples, Data management and query system OLTP and OLAP and Their data models, Data warehousing, ETL and data integration Dashboard creation using Tableau, Concepts of Business intelligence (BI), the relevance of BI in application to analytics industry and different domains.
