Get the most demanding course certification on your resume.
Become an MDAE’s certified data science specialist. The job of Data scientist has already become the hottest job in recent times because each business requires data-driven decision making. Companies require an employee with the capability to analyze data and present insights to make decisions. Data scientists have skills in mathematics, statistics, analytics, modeling, and have business acumen. These skills help them to do business forecasting and identify new market opportunities. Our data science specialist course will cover modules for programming, mathematics, visualization techniques, statistics and machine learning. Learn how to apply these technologies to solve real world business cases.
According to the Glassdoor, in 2021 data science is the highest paid field. According to their findings, the national average salary for a Data Scientist is INR 10,00,000 per year in India. Data Scientist works for top companies worldwide like Google, Microsoft, Facebook, Infosys, TCS etc. and the national average salary for a Data Scientist is $1,20,931 in the United States coming to Europe, national average salary for a Data Scientist is €52,000. These salaries are much higher compared to other jobs.
Assistant Professor, MDAE
Manager in L&T financial
Masters and M.Phil in Economics
Module 1: Introduction to R Programming
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
Basic mathematical and statistical operations- Calculating measures of central tendency and dispersion
Importing data from an excel sheet into R and exporting data from R to excel
Handling missing values
Writing functions, if-else statements and generating loops
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
Module 2: Data Wrangling and Visualization with R
Scatter plots, lines, steps, box plots, adding parameters to the basic plot function- creating legends, adding colors, grid lines, viewing multiple graphs in a single pane, etc.
Bar plots, histograms, pie charts, quintiles, Percentiles, Q-Q plots, function curves
Basic line graphs, scatters, regression lines, balloon plots, etc. with ggplot2
Bar plots, correlation matrix, functions, network graphs
Heat maps, 3-dimensional and animated graphics
Chloreopath maps and creating maps from a shape file
Data wrangling part 1
Data wrangling part 2
Module 3: Quant Essentials for data science
Introduction to Graphs – the why and how
Graphs of Common Functions.
To look at Data and be able to identify the nature of function which can best fit the data
Concept of Derivative – What it means and how to find derivatives of common functions.
The intuition behind rules for finding derivatives. Optimization using derivatives
Concept of partial derivatives and uses in optimization
Introduction to Integration.
Common tools and techniques used in the integration. Applications.
Formulating and solving a system of equations – the role of determinants and matrices.
Principles of Counting – Permutations Combinations with Lots of Interesting and Practical Examples. How the estimates help in determining complexities of algorithms.
Introduction to Probability Theory with Lots of interesting examples. Conditional Probability.
Representing data – common techniques to represent data.
Random Variables – Discrete and Continuous. Measures of Central Tendency.
Binomial, Poisson Distributions – Applications.
Introduction to the Normal Distribution.
Solving problems using tables, excel, calculators.
Hypothesis Testing – z test, t-test.
Point Estimates, Confidence intervals, p-values.
Hypothesis testing – Chi-Square test.
Linear Regression – Concept of fitting a line to a given data.
Testing for correlation coefficients.
Module 4: Machine Learning with R
Content to be updated soon
A company sells 1000 washing machines per year, on an average. Getting them all at once will reduce the per order cost and headaches, but then there is a warehousing cost. What is the ideal quantity to order based on other parameters? What are these other parameters?
There is going to be a closely contested election at the state level. A simple sample of 500 people polled gives the candidate 52% of the votes. Is that enough to sit back and relax? What could be the sample size to give the candidate the confidence needed to take further action?
A person claims that she has ESP. Can we devise a test to check the claim. If probability dictates that she will be right 25% of the time, and she is right 27% of the time when asked to perform the experiment 1000 times, is that enough proof to validate her claim? What about 28%? What is that magic number when we can accept her claim? And even if we do, can we still be wrong? What is that margin of error?
MDAE alumni working in diverse roles across leading companies.
Head – Treasury Economics and Strategy, RIL
We have had a very good experience with a student from MDAE who has been working with us over the past three years. Practical exposure is what differentiates MDAE students, and we observed that she was able to close the gap between academia, and real-life faster than fresh graduates from other renowned institutes. MDAE is doing a stellar job in preparing candidates for the job market.
Chief Economist Crisil Ltd
With sound training in economic concepts, practical experience and exposure to real-world issues. MDAE students appeared well prepared for the job market.
CEO & Senior fellow at IDFC Institute
We had an excellent experience working with a student from the MDAE. Unlike most, he had an extraordinary curiosity and a willingness to learn and place textbook economics in their historical and political context, which itself involved digging into 500 years of political history. Based on this experience of working with him, we have made him an offer to join us full-time once he graduates from MDAE
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