Showing posts with label Coursera. Show all posts
Showing posts with label Coursera. Show all posts

Monday 26 February 2024

IBM Data Analytics with Excel and R Professional Certificate

 


What you'll learn

Master the most up-to-date practical skills and knowledge data analysts use in their daily roles

Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter

Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, & creating charts

Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny

Join Free: IBM Data Analytics with Excel and R Professional Certificate

Professional Certificate - 9 course series

Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months.

Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language. If you’re interested in using Python, please explore the IBM Data Analyst PC. 

This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.

You’ll learn the latest skills and tools used by professional data analysts and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter Notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process,  including data preparation, statistical analysis, data visualization, predictive modeling and creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.

This program is ACE® and FIBAA recommended—when you complete, you can earn up to 15 college credits and 4 ECTS credits.

Applied Learning Project

You will complete hands-on labs to build your portfolio and  gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

Projects include:

Analyzing fleet vehicle inventory data using pivot tables.

Using key performance indicator (KPI) data from car sales to create an interactive dashboard.

Identifying patterns in countries’ COVID-19 testing data rates using R.

Using SQL with the RODBC R package to analyze foreign grain markets.

Creating linear and polynomial regression models and comparing them with weather station data to predict precipitation.

Using the R Shiny package to create a dashboard that examines trends in census data.

Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and a dynamic Leaflet map widget to investigate how weather affects bike-sharing demand.

Predict Sales Revenue with scikit-learn

 


What you'll learn

Build simple linear regression models in Python

Apply scikit-learn and statsmodels to regression problems

Employ explorartory data analysis (EDA) with seaborn and pandas

Explain linear regression to both technical and non-technical audiences

Join Free: Predict Sales Revenue with scikit-learn

About this Guided Project

In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. 

By the end of this course, you will be able to:

- Explain the core ideas of linear regression to technical and non-technical audiences
- Build a simple linear regression model in Python with scikit-learn
- Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas
- Evaluate a simple linear regression model using appropriate metrics

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Jupyter and Python 3.7 with all the necessary libraries pre-installed.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Generative AI: Enhance your Data Analytics Career

 


What you'll learn

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 

 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Join Free: Generative AI: Enhance your Data Analytics Career

There are 3 modules in this course

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. 

In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries.

You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more.

Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. 

This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.

Data Analyst Career Guide and Interview Preparation

 


What you'll learn

Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Join Free: Data Analyst Career Guide and Interview Preparation

There are 4 modules in this course

Data analytics professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field?

This course will prepare you to enter the job market as a great candidate for a data analyst position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. 

The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a take-home challenges and get to practice completing them. Additionally, it provides information about the regular functions and tasks of data analysts, as well as the opportunities of the profession and some options for career development.

You will get guidance from a number of experts in the data industry through the course. They will discuss their own career paths and talk about what they have learned about networking, interviewing, solving coding problems, and fielding other questions you may encounter as a candidate. Let seasoned data analysis professionals share their experience to help you get ahead and land the job you want.

Machine Learning With Big Data

 


Build your subject-matter expertise

This course is part of the Big Data Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Machine Learning With Big Data

There are 7 modules in this course

Want to make sense of the volumes of data you have collected?  Need to incorporate data-driven decisions into your process?  This course provides an overview of machine learning techniques to explore, analyze, and leverage data.  You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

At the end of the course, you will be able to:

Design an approach to leverage data using the steps in the machine learning process.
Apply machine learning techniques to explore and prepare data for modeling.
Identify the type of machine learning problem in order to apply the appropriate set of techniques.
Construct models that learn from data using widely available open source tools.
Analyze big data problems using scalable machine learning algorithms on Spark.

Tuesday 20 February 2024

Cybersecurity Attack and Defense Fundamentals Specialization

 


What you'll learn

Information security threats, vulnerabilities, and attacks.

Network security assessment techniques and tools.

Computer forensics fundaments, digital evidence, and forensic investigation phases.

Join Free: Cybersecurity Attack and Defense Fundamentals Specialization

Specialization - 3 course series

This Specialization can be taken by students, IT professionals, IT managers, career changers, and anyone who seeks a cybersecurity career or aspires to advance their current role. This course is ideal for those entering the cybersecurity workforce, providing foundational, hands-on skills to solve the most common security issues organizations face today.


This 3-course Specialization will help you gain core cybersecurity skills needed to protect critical data, networks, and digital assets. You will learn to build the foundation that enables individuals to grow their skills in specialized domains like penetration testing, security consulting, auditing, and system and network administration. 

Applied Learning Project

Learn to troubleshoots  network security problems, monitor alerts, and follow policies, procedures, and standards to protect information assets. You will gain practical skills cybersecurity professionals need in Information Security, Network Security, Computer Forensics, Risk Management, Incident Handling, and the industry best practices.

Cybersecurity: Developing a Program for Your Business Specialization

 


Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from University System of Georgia

Join Free: Cybersecurity: Developing a Program for Your Business Specialization

Specialization - 4 course series

Cybersecurity is an essential business skill for the evolving workplace. For-profit companies, government agencies, and not-for-profit organizations all need technologically proficient, business-savvy information technology security professionals. In this Specialization, you will learn about  a variety of processes for protecting business assets through policy, education and training, and technology best practices. You’ll develop an awareness of the risks and cyber threats or attacks associated with modern information usage, and explore key technical and managerial topics required for a balanced approach to information protection. Topics will include mobility, the Internet of Things, the human factor,  governance and management practices.

Enterprise and Infrastructure Security

 


Build your subject-matter expertise

This course is part of the Introduction to Cyber Security Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Enterprise and Infrastructure Security

There are 4 modules in this course

This course introduces a series of advanced and current topics in cyber security, many of which are especially relevant in modern enterprise and infrastructure settings. The basics of enterprise compliance frameworks are provided with introduction to NIST and PCI. Hybrid cloud architectures are shown to provide an opportunity to fix many of the security weaknesses in modern perimeter local area networks.

Emerging security issues in blockchain, blinding algorithms, Internet of Things (IoT), and critical infrastructure protection are also described for learners in the context of cyber risk. Mobile security and cloud security hyper-resilience approaches are also introduced. The course completes with some practical advice for learners on how to plan careers in cyber security.

Monday 19 February 2024

Advanced Django: Advanced Django Rest Framework

 


What you'll learn

Optimize the Django Rest Framework

Integrate with ReactJS

Join Free: Advanced Django: Advanced Django Rest Framework

There are 4 modules in this course

Code and run Django websites without installing anything!

This course is designed for learners who are familiar with Python and basic Django skills (similar to those covered in the Django for Everybody specialization). The modules in this course cover testing, performance considerations such as caching and throttling, use of 3rd party libraries, and integrating frontends within the context of the Django REST framework.

To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to slowly building features, resulting in large coding projects at the end of the course.

Course Learning Objectives: 

Write and run tests on Django applications
Optimize code performance using caching, throttling, and filtering
Use a 3rd Party library
Integrate with common Frontends

Select Topics in Python Specialization

 


What you'll learn

Create websites with Django

Create charts and plots with Matplotlib and Jupyter notebooks

Create a chatbot with the NLTK library

Join Free: Select Topics in Python Specialization

Specialization - 4 course series

This specialization is intended for people who are interested in furthering their Python skills. It is assumed that students are familiar with Python and have taken the Programming in Python: A Hands-On Tutorial.

These four courses cover a wide range of topics. Learn how to create and manage Python package. Use Jupyter notebooks to visualize data with Matplotlib. The third course focuses on the basics of the Django web framework. Finally, learn how to leverage Python for natural langauge processing.

Applied Learning Project

Learners create a variety of projects from their own Python packages, as well as use third-party package management tools. They also transform data into different charts and plots. In the Django course, learners build three simple websites. Finally, natural language processing powers a chatbot that learners build.

Web Applications and Command-Line Tools for Data Engineering

 


What you'll learn

Construct Python Microservices with FastAPI

Build a Command-Line Tool in Python using Click

Compare multiple ways to set up and use a Jupyter notebook

Join Free: Web Applications and Command-Line Tools for Data Engineering

There are 4 modules in this course

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.

Database Engineer Capstone

 


What you'll learn

Build a MySQL database solution.

Deploy level-up ideas to enhance the scope of a database project.

Join Free: Database Engineer Capstone

There are 4 modules in this course

In this course you’ll complete a capstone project in which you’ll create a database and client for Little Lemon restaurant.

To complete this course, you will need database engineering experience.  

The Capstone project enables you to demonstrate multiple skills from the Certificate by solving an authentic real-world problem. Each module includes a brief recap of, and links to, content that you have covered in previous courses in this program. 

In this course, you will demonstrate your new skillset by designing and composing a database solution, combining all the skills and technologies you've learned throughout this program to solve the problem at hand. 

By the end of this course, you’ll have proven your ability to:

-Set up a database project,
-Add sales reports,
-Create a table booking system,
-Work with data analytics and visualization,
-And create a database client.

You’ll also demonstrate your ability with the following tools and software:

-Git,
-MySQL Workbench,
-Tableau,
-And Python.

Web Application Technologies and Django

 


What you'll learn

Explain the basics of HTTP and how the request-response cycle works

Install and deploy a simple DJango application

Build simple web pages in HTML and style them using CSS

Explain the basic operations in SQL

Join Free: Web Application Technologies and Django

There are 5 modules in this course

In this course, you'll explore the basic structure of a web application, and how a web browser interacts with a web server. You'll be introduced to the Hypertext Transfer Protocol (HTTP) request/response cycle, including GET/POST/Redirect. You'll also gain an introductory understanding of Hypertext Markup Language (HTML), as well as the overall structure of a Django application.  We will explore the Model-View-Controller (MVC) pattern for web applications and how it relates to Django.  You will learn how to deploy a Django application using a service like PythonAnywhere so that it is available over the Internet. 

This is the first course in the Django for Everybody specialization. It is recommended that you complete the Python for Everybody specialization or an equivalent learning experience before beginning this series.

Fundamentals of Machine Learning in Finance

 


Build your subject-matter expertise

This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Fundamentals of Machine Learning in Finance

There are 4 modules in this course

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.  

A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.
Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The course is designed for three categories of students:
Practitioners working at financial institutions such as banks, asset management firms or hedge funds
Individuals interested in applications of ML for personal day trading
Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance  

Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

Python and Machine Learning for Asset Management

 


What you'll learn

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets  

Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes    

Utilize powerful Python libraries to implement machine learning algorithms in case studies    

Learn about factor models and regime switching models and their use in investment management    \

Join Free: Python and Machine Learning for Asset Management

There are 5 modules in this course

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.

The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. 

We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis.

You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept.

At the end of this course, you will master the various machine learning techniques in investment management.

Python for Finance: Beta and Capital Asset Pricing Model


 What you'll learn

Understand the theory and intuition behind the Capital Asset Pricing Model (CAPM)

Calculate Beta and expected returns of securities in python

Perform interactive data visualization using Plotly Express

Join Free: Python for Finance: Beta and Capital Asset Pricing Model

About this Guided Project

In this project, we will use Python to perform stocks analysis such as calculating stock beta and expected returns using the Capital Asset Pricing Model (CAPM). CAPM is one of the most important models in Finance and it describes the relationship between the expected return and risk of securities. We will analyze the performance of several companies such as Facebook, Netflix, Twitter and AT&T over the past 7 years. This project is crucial for investors who want to properly manage their portfolios, calculate expected returns, risks, visualize datasets, find useful patterns, and gain valuable insights. This project could be practically used for analyzing company stocks, indices or  currencies and performance of portfolio.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Saturday 17 February 2024

Saturday 10 February 2024

Software Developer Career Guide and Interview Preparation

 

What you'll learn

Describe the role of a software engineer and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Join Free : Software Developer Career Guide and Interview Preparation

There are 3 modules in this course

Software engineering professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field?

This course will prepare you to enter the job market as a great candidate for a software engineering position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. 

The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a code challenge and get to practice completing them. Additionally, it provides information about the regular functions and tasks of software engineers, as well as the opportunities of the profession and some options for career development.

You will get guidance from a number of experts in the software industry through the course. They will discuss their own career paths and talk about what they have learned about networking, interviewing, solving coding problems, and fielding other questions you may encounter as a candidate. Let seasoned software development professionals share their experience to help you get ahead and land the job you want.  

This course will prepare learners for roles with a variety of titles, including Software Engineer, Software Developer, Application Developer, Full Stack Developer, Front-End Developer, Back-End Developer, DevOps Engineer, and Mobile App Developer.

Thursday 8 February 2024

Post Graduate Diploma in Applied Statistics

 

Earn a Post Graduate Diploma from a premier institution and build skills for a successful career in data science.

By joining this Postgraduate Diploma program, you will be empowered with the statistical tools required to make data-driven decisions and advance your career in the fields of data science and applied statistics. You will also hone your skills with real-world data from governments and international organisations.

  • Learn how to analyse, visualise, and present large data sets: You will benefit from a 360-degree view into how official data systems are built and learn scientific ways of collecting, analysing and presenting data.
  • Select a specialised track: You will start with the foundations of statistics, economics, and computing skills, leading to a choice between two tracks - data analytics or official statistics.
  • Gain job-ready applied skills: Develop experience with data analysis tools in popular coding platforms like Python and R. You will also acquire skills needed to build, interpret and improve official databases used in policy making.

How will you benefit from this Post Graduate Diploma by ISI?

Learn from the Institution that works with the government and trains key officers of the Ministry :

Functioning under the Ministry of Statistics and Programme Implementation, ISI is a national institution that leads high-impact national projects involving very large data-sets and works very closely with the Government on various projects.

Prepare for the real-world by working with data from large government and international projects :

Work with open databases used in policy and decision-making across national public projects like the Census of India, National Coal Index (used by the Govt. of India in its auction process of coal mines), browser-based data capture technology for the NSSO survey and important crypto security projects.

Interact in live sessions with renowned, globally-recognised faculty :

Faculty at ISI include eminent scientists of global repute, whose contributions have been recognised with awards such as the Padma Shri, S. S. Bhatnagar Award and the Fellowship of Indian Academy of Science. They facilitate policy decisions by working with large datasets and train officers of the Indian Statistical Service.

Develop industry-ready skills and learn from accomplished experts :

You will get regular opportunities to interact with senior officers of the Indian Statistical Service with up to 40 years of experience. Learn from industry experts through live, interactive sessions and leverage insights to solve critical business challenges at the workplace.

Access potential job opportunities facilitated by ISI :

Through the placement committee led by an ISI faculty convenor, you will get the chance to showcase your skills to prospective employers after building your ‘Learner Skills’ profile. Placements for all ISI programmes are an entirely student-driven activity - there is no placement guarantee offered by ISI.

Receive exclusive career readiness support :

The career readiness program is designed to equip you with the essential skills and knowledge needed to thrive in today's competitive job market. You will receive exclusive access to networking opportunities, career workshops, sessions with industry experts, curated workplace success courses and mentorship from industry veterans.You will be equipped with the skills, connections, and knowledge needed to accelerate your career.

Gain access to an exclusive student community and alumni network :

  1. Gain a global perspective to data science - 27% learners are studying from 15 different nations such as the USA, UK, Norway, Germany, Japan, Sweden, Australia, etc.

  2. Learners have rich industry experience - 80%+ of the batch are working professionals seeking to advance their skills and career (36% have 10+ years of experience). 40% of the batch is 35+ years old.

  3. Network with industry leaders - Many learners are senior professionals at large MNCs and PSUs such as Microsoft, IBM, Accenture, EY, Volvo, BlackRock, Tata Motors and Wipro.

ISI on-campus graduates consistently go on to succeed as data scientists, analysts, statisticians, researchers, policymakers, and more, and have taken roles with industry leaders such as Microsoft, Google, Dell, JP Morgan & Chase, KPMG, Amazon, Flipkart, Samsung, and others.

Explore enhanced flexibility features :

  1. Payment flexibility - Choose to pay in instalments rather than paying for the entire program upfront to better plan and finance your education.
  2. Self-paced learning - Complete your studies in up to 36 months at no additional cost - focus on your academic pursuits without compromising on your other commitments.
  3. Classes that suit your schedule - Manage your work and studies better by attending classes in the evenings and on weekends.
  4. Choose your specialisation - Choose amongst either data analytics or official statistics (or both), depending on your area of interest.

JOIN: Post Graduate Diploma in Applied Statistics Indian Statistical Institute



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