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Certification Course in


Data Analytics for Business

Shaheed Sukhdev College of Business Studies (CBS) is a premier undergraduate management college under the aegis of the University of Delhi (DU) offering Bachelor of Management Studies (BMS), BBA (Financial Investment Analysis), B. Sc. (H) Computer Science and PG diploma in Cyber Security and Law. The admission in BMS and BBA (FIA) is through a highly competitive Joint Admission Test (DU JAT) which comprehensively encompasses business acumen, logical reasoning, verbal ability and quantitative ability. The college is known for its unique pedagogy, a combination of theoretical knowledge and its practical application in real world, and the state-of-the-art infrastructure the campus boasts. The esoteric knowledge of data analysis and machine learning becomes a quintessential tool to stand apart in today's competitive environment. In order to enable the same, SSCBS is proud to introduce a one-of-a-kind data analytics course certified by the Shaheed Sukhdev College of Business Studies University of Delhi, housed at our very own state-of-the-art campus!

Objective

Expecting to build a solid foundation of business analytics, this course has been designed to impart knowledge of machine learning and statistical methods for data analysis. The course shall also 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.

Course Duration : 125 hours

Fee : INR 40,200

Registration fee : INR 200

Tuition Fee : INR 40,000

Target Audience :

  1. Students who have passed 10+2 examinations with Mathematics.

  2. Professionals having knowledge of Mathematics.

Course Structure

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.

Introduction to Python Editors & IDE’s (Jupyter, Spyder, pycharm, etc.), custom environment settings, basic data types (numeric, string, float) 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

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).

ntroduction, 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, Introduction to Stochastic models, Markov models, Classification of states, Steady-state probability, Dynamic Programming.