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MOS - Microsoft Office Specialist, IC3 - Internet and Computing Core Certification, and DDE - Diploma in Data Entry.
OBJECTIVE
This course will teach how to program in R and how to use R for effective data analysis. The students will learn how to install and configure R necessary for an analytics programming environment and gain basic analytic skills via this high-level analytical language. The course covers fundamental knowledge in R programming. Popular R packages for data science will be introduced as working examples.
2 Months /72 Hrs.
MODULE 1: INTRODUCTION TO R PROGRAMMING
Overview operator’s data structurer Work space, Installation of R studio, comparison of R vs Python, Know to things before start learning R, introduction to descriptive and inferential statistics, Basics skills required for R. The real life example for R usage.
MODULE 2: VISUALIZING DATA: GRAPHS & CHARTS
Tables, charts and plots. Visualizing Measures of Central Tendency, Variation, and Shape. Box plots, Pareto diagrams. How to find the mean, median standard deviation and quantiles of a set of observations? Students may experiment with real as well as artificial data sets
MODULE 3: PROBABILITY DISTRIBUTIONS, RANDOM SAMPLES.
Set operations, simulation of various properties. Bays’ rule. Generate and Visualize Discrete and continuous distributions using the statistical environment. Demonstration of CDF and PDF uniform and normal, binomial Poisson distributions. Students are expected to generate artificial data using the chosen statistical environment and explore various distribution and its properties. Various parameter changes may be studied. Study of binomial distribution. Plots of density and distribution functions. Normal approximation to the Binomial distribution. Central limit theorem. How to generate random numbers. Study how to select a random sample with replacement from normal and uniform distribution. Students can use the built in functions to explore random sample selection. How to calculate the correlation between two variables? How to make scatter plots? Use the scatterplot to investigate the relationship between two variables. How to calculate and plot the residual
MODULE 4: STUDY OF CONFIDENCE INTERVALS.
How to compute confidence intervals for the mean when the standard deviation is known. How to perform tests of hypotheses about the mean when the variance is known. How to compute the p-value? Explore the connection between the critical region, the test statistic, and the p-value.
MODULE 5: HOW TO FIND QUARTILES OF THE T-DISTRIBUTION.
How to perform a significance test for testing the mean of a population with unknown standard? deviation. Compare populations means from two Normal distributions with unknown variance Tests of Hypotheses for One Proportion, Tests of Hypotheses for Comparing Two Proportions
MODULE 6: DATA ANALYTICS USING R
Introduction to data science and data mining, Statistical learning vs Machine learning, Big data predictive analysis, Regression, Classification, clustering case studies: Data analysis, Mining stream data, Social Network.
OBJECTIVE
This is an advanced course which will helps you to give the basics of excel and covers all the enriched features in Microsoft excel 2016, including macros, pivot table, audit & analyze worksheets, use advance formulas & functions, work with multiple worksheets and workbooks etc. which in turn increases productivity, improves efficiency by streaming the workflow thus becomes a major asset for professional employees.
2 Months /72 Hrs.
Introducing excel
Entering and editing worksheet data
Essential worksheet operations
Working with cells and ranges
Introducing tables
Worksheet formatting
Understanding excel files
Using and creating templates
Printing your work
Working with formulas/functions
Conditional formatting
Data sorting and filtering
Charts
Auditing
Pivot tables
Pivot charts
Working with data
What-if analysis
Exporting and importing
Reviewing a workbook/worksheet
Vba macro