Shaheed Sukhdev College Of Business Studies : Data Analytics Admissions

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Shaheed Sukhdev College of Business Studies: SSCB is a leading institution of the University of Delhi. It was organized in August 1987 by the Delhi Administration on the initiation of UGC and the Ministry of Human Resources, CBS, in a short period of 25 years, established itself as the premier undergraduate management school.  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.

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The institution is better known for its unique pedagogy, a combination of theoretical knowledge and its practical application in the genuine world, and the state of the art infrastructure the campus boasts.

SSCBS is proud to introduce a one-of-a-kind Data Analytics Course certified by the University of Delhi. The 125-hour course has been made under the guidance of professors from some of the leading institutes of the country, including IIMs and IITs, with the focus on machine learning and statistical methods for data analysis. The course also offers a first-hand learning experience of python programming language & python/R programming.

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Shaheed Sukhdev College of Business Studies Important Dates

Event Date
Admission Begin November 2019

Course Duration: 125 hours

SSCBS Data Analytics Admission Course fee

Registration Fee Rs. 200
Tuition Fee Rs. 40,000

Eligibility Criteria

Applicant must have completed his/her 10+2 with Mathematics.

Course Contents

  • Business Analytics
  • Data manipulation using Python
  • Data Analysis
  • Machine Learning
  • Optimization in Analytics

Shaheed College of Business Studies Course Structure

Module 1: Introduction to Business Analytics

  • Descriptive Analytics: Explaining and summarizing datasets, measures of central tendency, dispersion, skewness, kurtosis, Correlation.
  • Probability: Scope 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.

Module 2: Data Manipulation Using Python

Addition 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 actions for data conversion, writing user-defined functions.

  • Concept of Packages/ Libraries: Necessary packages such as NumPy, SciPy, sci-kit-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.

Module 3: Data Analysis

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

  • Data Pre Processing and Cleaning: Data manipulation track (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 (crosstabs, distributions and relationships, graphical analysis).

Module 4: Machine Learning- Part 1

Addition to 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.

Module 5: Machine Learning Part- 2

  • 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.
  • Unsupervised Machine Learning: Introduction, Clustering, K-Means algorithm, Affinity Propagation, Agglomerative Hierarchical, DBSCAN, Dimensionality Reduction using Principal Component Analysis.
  • 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.

Module 6: Optimization in Analytics

Addition 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.

For more information, visit http://sscbsdu.github.io/data-analytics-course/

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