If you have a passion for data analysis and want to build a career in the field of business analytics, this program is tailor-made for you. Gain the necessary skills to excel in data-driven decision making and unlock a world of opportunities.
Already in a business or analytical role but aiming to specialize in business analytics? This program is designed to enhance your analytical skills and empower you to drive data-centric strategies within your organization.
Midway in your career and eager to transition into the high-demand domain of business analytics? This program will equip you with the expertise needed to thrive in data-driven industries and propel your career forward.
Immerse yourself in the dynamic realm of business analytics, where data insights and statistical techniques drive critical business decisions. Explore the power of data visu-alization, predictive modeling, and machine learning to solve complex business chal-lenges.
Growing Demand: In today’s data-centric world, businesses seek skilled analysts to interpret and leverage data for competitive advantage.
Lucrative Salaries: Business analysts are in high demand, commanding attrac-tive remuneration and growth opportunities.
Decision-Making Power: By interpreting data, you can contribute to strategic decisions that shape business outcomes.
Versatility: Business analytics skills are applicable across industries, opening doors to diverse career options.
Acquire proficiency in data wrangling, exploration, andvisualization using tools like Tableau and Power BI.
Learn how to build predictive models and use statistical techniquesto forecast future trends and outcomes.
Understand the fundamentals of machinelearning and apply algorithms to solve real-world business problems.
Gain expertise in R programming language, widelyused for data analysis and statistical computing.
Program Duration & Format6-month online program.
Program Start DateFor information on the next start date, please refer to our website.
Tuition FeeThe total program fee is 60,000, inclusive of all taxes. This investment inyour future includes all the course materials and access to our renownedfaculty.
Installing Python on your computer; Jupiter notebooks. Using pip or similar command for installing various packages such as Pandas Matplotlib or Numpy. Reading data from csv files into Panda data frame. Using Matplotlib to plot one or more series of data. Descriptive statistics. Linear regression and Logistic regression.
Probability, Games of Chance, Conditional, Marginal and Joint Probability, Bayes’ Law, Sampling methods and empirical rules, sample mean and central limit theorem, standard normal distribution and probability table. Student’s t-distribution.
Framing mutually exclusive hypothesis, setting a significance level for type I error and typeII error. Testing for means – single sample, testing for proportions – single sample. Testingfor difference of means – two largesamples, testing for difference of proportions – two large samples, smallsamples and t- test statistic. hypothesis tests for paired samples, Chi-square test for independence of population attributes. Chi-square test for goodness of fit of distribution, correlation coefficient for categorical data
scatterplots and regression line, ordinary least squares regression, estimating the coefficients, testing for their significance, coefficient of determination, F statistic and fitness of overall model. Applications of regression, multiple regression, estimating betas for multiple regression. Odds, log of odds. Sigmoid function, Classification with logistic regression; Issues using regression for binary classification.
Installing R and/or RStudio on your computer. Installing packages. Reading csv files into R. Calculating summary measures. Counts and pivot tables in R. Sampling from various distributions. Hypothesis tests. Linear regression and logistic regression
Experimental design, completely randomised design, randomised blockdesign, factorial experiment, one way ANOVA for more than two populations, Pearson’s correlation coefficient, Spearman’s rank correlation.
Market Basket Analysis and Associative Rule Mining, Text analysis, Decision Trees, Data Visualisation, Cluster Analysis
Qualitative methods and quantitative methods. Method of Delphi, Jury ofExecutive Opinion etc. Calculating a moving average. Exponentialsmoothing. Exponential smoothing with trend and seasonalitycomponents.
Time Series Analysis, seasonality, stationarity, decomposition a series intotrend, seasonality, cycles and random variation. Box-Jenkins methodology.Dealing with non-stationarity, Tests for stationarity.