This course will introduce beginners in programming to the best practices of coding and the basics of statistical analysis and data handling in R. By the end of this course, participants would have taken their first step towards a career in data science or data analysis.
This course is an essential primer for anyone who wishes to pursue the R track.
Assistant Professor, MDAE
Dr. Sandhya Krishnan has taught data analytics and R programming at various places across India, and to a wide range of audiences including undergraduate and graduate students, faculty and other professionals. She believes that interesting stories are hidden behind numbers and anyone with a flair for numbers and the right training in data analytics can unravel these stories and present them to the world. She holds a Ph.D. from the University of Amsterdam and an MA in Economics from the University of Mumbai.
Assistant Professor, Fergusson College
Dr. Ishita Ghoshal teaches Macroeconomics, Econometrics and Mathematical Economics and conducts workshops on research methods, computer applications for Economics, applied Econometrics with various software like R, EViews, STATA, etc. Her areas of interest are Macroeconomics, Applied Econometrics and International trade. She holds a Ph.D. in Economics from the Gokhale Institute of Politics and Economics.
Associate, IDFC Institute
Mr. Ayush Patel is an RStudio certified tidyverse instructor. He enjoys teaching data analysis skills using R. Ayush has worked in the public policy sector with elected officials, state governments and think tanks. His work is focused on the intersection of development, law and economics. He has received training in engineering and economics.
Introduction to R console and editor, idea of packages in R
Types of data- integers, characters, numeric, logical, factor, dates, etc.
Types of objects: lists, vectors, matrices, data-frames, etc.
Creating and working with simple vectors
Merging vectors into a single data set
Importing data from an excel sheet into R and exporting data from R to excel
Assigning column names, extracting individual rows and columns
Basic mathematical and statistical operations- adding and subtracting across row and columns, calculating measures of central tendency and dispersion
Introduction to datasets in R packages
Dealing with missing data
Writing functions and if-else statements
Strings and operations on strings
Introducing the basic plot function- scatter plots, lines, steps, box plots
Adding parameters to the basic plot function- creating legends, adding background colors, grid lines, viewing multiple graphs in a single pane, etc
Bar plots, histograms, pie charts
Quintiles, Percentiles, Q-Q plots
Probability- counting, generating random variables, discrete and continuous probability functions
ANOVA, linear regression and its diagnostic checks and plots
Based on the stock portfolio held by a set of investors, how would differentiate between the risk takers and risk averse investors? How would you write a program in R to establish this?
It is required to understand whether the current pandemic and the shift in mode of learning has affected the grades of the students. How would you comment on the issue (with confidence and conviction) based on your data?
The Census of India publishes data on the distance of each village to the closest town. How would you identify and count the number of missing values in this data and then summarize it at the district level to obtain mean, median and standard deviation of the distance to closest town?
MDAE alumni working in diverse roles across leading companies.
Trainee Decision Scientist
Senior Research Analyst
MDAE’s learning journey offers a candidate multiple entry and exit points. For those with prior exposure to the field, they can choose one or more of the courses on offer as long as they meet the course prerequisites. For example – a candidate who understands the basics of programming, can straightway opt to study the wrangling course or even the machine learning course. On the other hand, the career track allows a participant to deep dive and go through a solid training right from the foundation to the advance level. The career track is a 120 hour, 4 course training program which allows the candidate to pick a programming language between R or Python. The first course (Quant essentials for data science) is common across the programming languages, and the next 3 courses are specific to what you choose (R or python)
There are 3 distinct advantages of choosing a career track over a skills track –
1) Different certification – With the integrated 4 course training, candidates will become an MDAE Certified Data Scientist – an industry demanded certificate.
2) Small group mentoring – Candidates will receive live mentoring with industry’s best practitioners throughout the program.
3) Capstone projects – Deserving candidates can be evaluated for projects/internships with our industry partners.