Graduate Data Analytics Certification Program


Certified Trainers

All our trainers have been vetted carefully by industry experts for their specialisation, teaching skills as well as years of experience. We put emphasis on actual learning outcomes of attendees with varied degrees of pace, pre-existing knowledge, and potential. In short, our approach is to leave no one behind!!! We are a team of young minds with high potential, backed by our enormously experienced advisory council members. We got you covered!

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certification

You don't need a PhD to become a professional data analyst. All you need is a quest for well-aligned and self-paced learning. No programming skills are required; except some elementary computer skills. We start small, re-enforce applied learning, using tools and techniques coupled with numerous hands-on exercises. Upon completion, we issue a professional certificate with an exclusive digital badge, recognising your proficiency.

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HANDS-ON Projects

In today's digital economy, three important competencies, namely, Critical Thinking, Collaboration, and Communication are essential, in addition to industry specific-skills. In order to develop these abilities through social and emotional learning, we encourage our learners to undertake extensive applied projects, headed by our industry experts and trainers. These projects equip you with the knowledge you need to thrive in the world of work.

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Skills You will gain

  • Directory structure for Projects
  • Directories for Collaborative Projects
  • Stata workflow and windows
  • Stata Shortcuts
  • Introduction to Cross-section, Time Series and Panel Data
  • Working with preinstalled data 
  • Importing non-stata files
  • Exporting data to other files formats
  • Temp file management
  • Do-files management for Accuracy, Efficiency, Automation, Usability, Scalability and Standardisation
  • Writing and Debugging Do-files
  • Automating Your Work
  • Working with Useful Stata Commands
  • Creating random data and random samples
  • Introduction to Large Datasets and Document Evaluation
  • Truncating Dataset: Keep/drop and if/in qualifier
  • Append datasets
  • Merge Datasets
  • Collapse Datasets
  • Reshape Datasets
  • Egen Command Extension
  • Systematic data cleaning
  • Missing observations and codes
  • Computation by Groups
  • Verifying Values using Survey Questionnaire
  • Local and Global Macro
  • Loop Constructs
  • Foreach and Forvalue Functions
  • Creating Dictionary Files for Big Data Analytics
  • Importing Non-stata Text Format Files
  • Sorting and Filtering Big Data Files
  • Reshaping Large Data Files
  • Collapsing Data on Key Indicators
  • Working with National Sample Surveys Data
  • Working with Periodic Labour Force Surveys Data
  • working with Annual Survey of Industry data
  • working with economic census data
  • Survey Weights
  • Exploratory data analysis
  • Testing for normality 
  • Linear Regression Analysis
  • Multiple Regression Analysis 
  • Dummy Variables
  • Regression with more than one dummy variable
  • Interaction/moderation effect
  • Linear Regression Assumptions and Diagnostics
  • Functional form misspecification
  • Endogeneity and measurement error
  • Outlier statistics and measures of leverage
  • The generalized linear regression model
  • The robust estimator of VCE
  • Testing for serial correlation
  • Introduction to Binary Variables
  • LPM Models and their limitations
  • Probit and Logit Models
  • Multinomial Models
  • Odds Ratio, Marginal Effects and Goodness of Fit
  • Endogeneity Models
  • Two Stage Least Square Model
  • Time-series data
  • Pooled cross-sectional time-series data
  • White noise, autocorrelation, and stationarity 
  • Univariate Time-Series Models
  • Multivariate and Multiple Equation Models
  • Modeling Non-Stationary Time-Series Processes
  • VaR estimation
  • ARCH and GARCH models
  • Fixed-effect and Random-effect Models
  • One-way Fixed effect
  • Testing the appropriateness of Random effect
  • Instrumental variable models for panel data

Career Outcomes

  • Start a new career after completing this course
  • Prepare you for higher studies and meaningful decent jobs
  • Complete academic and professional projects diligently
  • Get a pay increase or promotion
  • Prepare you for bigger roles in the organisation
  • Get tangible career benefits from this course
  • Prepare you for life-long learning outcomes

course timeline

Week I

Part I: Introduction

We will teach you basics of stata such as workflow, types of windows, data formats etc.

Week I

Week II-III

Getting Started With Data Files

We will cover topics such as different data file structures, importing of various non-stata format files, exporting files to other formats and data exploration.

Week II-III

Week IV-V

Stata file formats

To simplify analysis, stata has included various windows for efficiently working with big datasets. Different stata files structures such as do files, data files, dictionary files, output files etc. will be taught in this section.

Week IV-V

Week VI-VII

Stata Playground

In this section, we will cover basics of data management such as generating new variables, changing existing variables, string to numeric transformation, recoding, encoding and decoding of variables for better analysis and other advanced useful stata commands.

Week VI-VII

Week VIII-X

Part II: Data Modification and Transformation

It’s relatively easy to work with single datafile. However, multiple data files make tasks little tedious if not understood the right techniques. It might distort your data while combining. We will be covering append, merge, collapse and reshape topics along with other essential commands in this section. 

Week VIII-X

Week XI

Data Cleaning

No dataset is perfect. Analysts face many challenges such as missing observations, incorrect variable coding, mismatch and non-numeric observations. We will be covering techniques to overcome these problems in this section.

Week XI

Week XII-XIV

The Basics of Stata Programming

Time is everything. Isn’t it amazing to cut down on repeated task and put your work on automation mode? This is what we will teach you in this section. Topics such as macros and loops will be covered. 

Week XII-XIV

Week XV

Part III: Working with Large Datasets: Data Extraction

You will be the first choice in the labour market, if you know how to deal with large datasets provided by national/international agencies. We will teach you everything about these datasets which often comes in the fixed file formats- not so easy to deal with. In short, we will address the elephant in the room. Skills which no one would like to share with you in the professional world.

Week XV

Week XVI-XVII

Data Preparation & Analysis

Now that we got the data in the stata file format, We will learn filtering techniques to transform it as per analysis needs. 

Week XVI-XVII

Week XVIII- XIX

Summary Statistics and Description of Datasets

Here begins the analysis and the first step in this direction is exploratory data analysis techniques using conditional functions and graphical representation. Remember, we have your back!

Week XVIII- XIX

Week XX-XXII

Part IV: Applied Regression Analysis

Data is just a collection of numbers on different attributes unless we can extract meaningful information out of it. To do so, we need applied regression tools other than descriptive statistics and cross-tabulation. We will cover topics such as linear regression, multiple regression, dummy regression and interaction effects to produce logical analysis. 

Week XX-XXII

week XXIII-XXIV

Linear Regression Assumptions and Diagnostics

Not all data fulfils the assumptions of the specified regression techniques which might influence our analysis and produce different results. In this process, we need to diagnose such techniques and try to find remedial solutions. We will cover topics within the limits of linear regression models in this section.

week XXIII-XXIV

Week XXV

The Functional Form Specification

What if we specify the wrong regression equation such an included the irrelevant variables or excluded the relevant one. Or it should have been linear but we specified non-linear. This will have serious implication for measurement errors. To error proof our models, we need to work on these techniques for accuracy and reliability.  

Week XXV

Week XXVI-XXVII

Part V: Non-Linear Estimation and Linear Models Extensions

Beyond linear regression models, generalised linear regression models as well as tests for robustness will be covered in this section in detail. 

Week XXVI-XXVII

Week XXVIII-XXIX

Discrete Response Models

Not all outcome variables can be continuous in nature. To deal with this issue, we will learn logistic regression models and their result interpretation using odds ratios as well as marginal effects for comparison across time and different model specification. 

Week XXVIII-XXIX

Week XXX-XXXI

Instrumental Variable (IV) Regression and Multi-Equation Models

To deal with the problem of endogeneity, we will introduce you to instrumental variable regression models as well as two-stage least square models. 

Week XXX-XXXI

Week XXXII-XXXIV

Part VI: Organising and Handling Economic and Financial Data

Time-series and panel data are quite different vis-a-vis cross-sectional data. They need different techniques for data management and manipulation. We will learn these techniques for time-series and panel data in this section.

Week XXXII-XXXIV

Week XXXV-XXXVII

Time Series Models

We will teach you concepts such as stationary time-series Vs. non-stationary time series, autocorrelation, white noise and forecasting fundamentals. Further, time-series models such as ARCH, GARCH and VAR estimations will be covered in this section.

Week XXXV-XXXVII

Week XXXVIII-XL

Panel Data Models 

To deal with panel data, we will teach you various application of fixed effect and random effect models as well as instrumental variable models for panel data.

Week XXXVIII-XL

Week XLI-L

Hands-on Projects

All the learners will be asked to apply techniques learnt in this program and complete individual exercises as well as group project for better understanding of the data analytics tools and techniques using real time big datasets. Upon completion of the course, all the learners will be issued completion as well as proficiency certificate based on individual performance. 

Week XLI-L

earn a certificate upon completion

Data Management and Visualisation

Course Participation Statistics

college students
1 %
Research scholars
1 %
Professionals
1 %

FAQs

The blend of your subject knowledge and use of STATA as a tool is what makes you an expert in applied data analytics.  STATA is used in over 180 countries, which makes it one of the widely used statistical software in the world. STATA provides an extensive list of statistical techniques that one can learn and take advantage of their use across various projects and day-to-day work in the fields of economics, business, education, political science, sociology, biostatistics, finance, marketing and management. 

Stata is an effective analytical and statistical tool and widely used in over 180 countries, across various national and international organisations. Using stata is highly effective and its multipurpose applications are advantageos; especially in the disciplines of education, medical, economics, business, marketing, biostatistics and behavioral sciences. Researchers in these fields have relied on stata for its accuracy, efficiency, extensibility and reproducibility. This software is also extensively employed for graphical illustration and visualisation, data management, research outputs, and regression modelling analysis. 

Stata is a statistical software for statistical analysis developed by statistician. On the other hand, Python is a programming language written for programming purposes by the programmers. R seems neither here nor there. In short, it is a programming language created by statisticians. With python gaining popularity for its use as a programming language, R is going through an existential crisis. If the major purpose is to do statistical analysis then, Stata is the only street boy who has beaten E-views and SPSS and all set for the big boys table with its latest Python integration support, meaning any exercise you do in Python now directly can be done in Stata. All in all, Stata is best known for its simplicity, ease of use, accuracy, efficiency and graphical user interface (GUI).

Learning stata and statistics are two different things. However, in order to learn more nuanced concepts and their applications in stata; the subject knowledge is required at the later stages of learning. Many people (irrespective of their degree courses) choose data analytics as a specialisation. Therefore, we have carefully designed our courses, keeping in mind pre-existing knowledge and acquisition pace of our learners. We start at a very basic level and cover all necessary statistical concepts using mixed approach. We don't just instruct coding skills, we teach concepts and data skills, which are rendered necessary to become skilled in data analytics. 

Stata being a statistical package is user-friendly with interactive interface (GUI as well as command-based window) and easy to learn from scratch. It is widely used in the research sector as well as in the corporate world. All the research centres of the major consulting firms, universities, government institutions, NGOs, international organisations; such as united nations, International Labour Organisation, World Bank, UNDP, WHO, etc., use stata for evidence-based data analysis and econometric modelling. 

Regression analysis, multivariate regression analysis, logit or probit regression, time-series models, panel-data models, structural equation models, instrumental variable models, generalised least square models, cross-sectional data management, capital asset pricing models, survey data management - you name it and it will produce results in the blink of an eye. Whether you are a political scientist, an economist, a business analyst, a statistician or a sociologist; you always need data-backed facts to support your well-thought narration, and stata makes it possible. 

Because It's All About Happy Students...

Initially, I wasn’t quite sure about training programs as I already had so much on my plate and I thought degree is more than enough to help me grab any opportunity in the market. Alas, I had a little setback when I couldn’t find internship to polish what I learnt during first two college years because there is a huge gap between what is being taught at the university level and what is actually being required at the work place. I did what I could, joined graduate level course on the recommendation of my friend. Not only that the young team at the outlier analytica work on the practical aspect of what you have learnt but also prepare you for the job market through their career service program. I will always be indebted for their services.

Ankit
University of Cambridge

Being a student from small town, I was never exposed to the college culture before. I had little knowledge of skills required to succeed. Though formal higher education is a must as it increases your chances of success but we need a lot more than that in this ever changing digital economy. I had idea about data analytics as being a student of economics major but I was confused between which software or programming language to learn. I wanted to have my options open for MBA as well as MSc. But to excel in both the streams, a little more than just coding or programming is required. This gap was fulfilled by Outlier as majority of the trainers are PhDs. Their tailor-made data analytics programs are essential for higher education aspirants. I finished my masters from DSE.

Ksheerja
Delhi School of Economics

The only question that bothered me always, how can I maximise the value of my limited stock of money? Either I can borrow and take admission in some MBA institution after finishing my undergrad or learn the skills which will increase my employability, take the job, earn money and then join executive MBA side-by-side. I was not very great in studies but never stopped hustling for my future. Today I am working as a teaching assistant at ISB, Hyderabad. I will always be grateful for their guidance. They will not make any false promises. Nor they will try to teach you data analytics in 10 hours or 20 hours as claimed by many other institutions. It’s a gradual process and if you are determined they will never leave your side. They are true mentors and understand the job market.

Abhishek
ISB, Hyderabad

Excellent quality content. Variety of courses that really get you interested in the topics and approaches of data analytics and research. Choose on the basis of your understanding and requirement as I am sure one size doesn’t fit all. All the courses are designed keeping in mind the interests of various types of learners; in such a way that broadens your understanding of the complex issues and problems and also develop critical thinking. They teach you various approaches to reframe problems and find their solutions. 

rishabh
university of delhi

I would highly recommend data analytics courses provided by outlier analytica to anyone curious in learning what data analytics is all about. The course instructors simplifies the contents that make you understand concepts easily. If you are a beginner, tools and materials provided by outlier are very helpful to start with. Beginning with the useful commands to manage different types of datasets to teaching econometric models and results interpretations, they cover almost everything.

Muskan
amity university

Excellent delivery of course content! The team outlier is quite vibrant in terms of knowledge, quest for learning and always ready to take challenges. I will not say that they are google or they know it all but they know “how to know” and tackle challenges and problems with creative solutions. I have worked with them previously. They always strive for excellence and never settle for anything less than anyone deserves; kind of a win-win situation for everyone. 

Amit
faculty, university of delhi

They have three different types of courses which you can classify into beginners, intermediate and advanced levels. Being a researcher, I was looking to work with large government datasets which comes in many different formats. Normally, it takes over 101-12 months to cover the basics of it. However, team outlier with vast years of experience into handling these large datasets, I was able to accomplish my goals pretty soon and concentrated on the real work of research. They make it easy and less time-consuming. Team has many years of experience working with international organisations which is a plus point if you are seriously looking for career in research sector. I would recommend their advanced level courses to anyone in the research sector. 

suresh
jawaharlal nehru university

I was mainly looking for career services which could help me getting into top research organisations such as UNDP, UN, World Bank, ADB, ILO etc. However, I wasn’t aware of their interview process mainly for their annual Young professional programs. Outlier not only guided me to prepare solid cover letter and CV but also helped me prepare for the competency based interview which is now becoming a norm across various international organisations. They have a long checklist of questions prepared for such interviews which is apt for anyone seeking to start their career with such organisations. It’s a thumbs up from my side if you are looking for either data analytics courses, internship, collaboration or career services.  Three cheers for team outlier!

Unnaty
boston university

I did not have any background in data analytics. It becomes little hard especially when you do not have statistical background. However, with increasing demand for such skills in the investigative journalism, you need more than microsoft excel skills and joined the beginners’ level course at outlier analytica. It cleared the basics of data analytics (i.e. built strong foundation) and made me aware about the correct path to learn data analytics and its career prospects across various industries in the long run. Overall, very good learning experience with the trainers and easy to understand course content, however, still a long way to go. I would recommend courses provided by outlier to anyone who is from non-statistical background. 

kedar
journalist

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