Become a Data Analyst - Job training (beginner to advanced)

For jobseekers and career change aspirants - including AI and Career Guidance Modules - Learn from Industry Leaders

Ratings: 5.00 / 5.00




Description

Welcome to 'Data Analyst - Job training', a first-of-its kind short program designed for jobseekers and career change aspirants.

This course is specifically created as a JOB-BASED TRAINING  program thereby teaching concepts hands-on and relevant to real-work environment. If you are looking for a job in Data Analysis or if you are a student who would like to get first experience in this domain, this course is exactly for you.


How is this program a JOB-BASED TRAINING and how will it equip you with skills relevant for your role:

What companies ask for a Data Analyst role?

  • Gather and collect data from various sources, ensuring data accuracy and completeness.

  • Clean and preprocess data to prepare it for analysis.

  • Clean and preprocess data to prepare it for analysis.

  • Create reports and dashboards to present key performance indicators (KPIs) and data-driven insights.

  • Apply statistical methods to analyze data and derive actionable insights.

  • Conduct hypothesis testing and regression analysis as needed.

  • Implement and maintain data quality standards.

How this course meets the requirements?

  • Learn how to import data in Python, usiung kaggle

  • Learn how to treat input data: distribution, outliers, null and missing values

  • Gain a deeper understanding in Descriptive Statistics

  • Master Data visualisation: Graphing etiquettes – which graphs are applicable for what type of data analysis

  • Gain knowledge on Descriptive Statistics, Inferential Statistics and Predictive Statistics

  • Understand Inferential statistics: Hypothesis testing, Normal distribution, Central LImit Theorem, Sample vs Population, Sampling, test statistics, Type I and II error

  • Learn and work with predictive analysis

Tools you will learn:

  1. Pandas

  2. Numpy functions

  3. Statistics

  4. Matplotlib

  5. Seaborn

  6. Python

  7. plotly

  8. dash

  9. Matplotlib

  10. Data Visualisation


Extra Module and Benefits:

1. AI Fundamentals and Applications:

Unlock exclusive access to one of our AI modules Learn from our experts leveraging AI to enhance your productivity and understand the wide variety of applications of AI across industries

2. Career Guidance

Understand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!


Trainers:

Dr. Chetana Didugu - Germany

Dr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.


Aravinth Palaniswamy - Germany

Founder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.

What You Will Learn!

  • Introduction to the field of Data Analysis
  • How to build an Analyst's mindset
  • An insight into the day-to-day tasks of a Data Analyst
  • Introduction to Descriptive Statistics
  • Terminology: Sample, Population, Statistic and Parameter
  • Classification
  • Prediction
  • Association
  • Pattern recognition
  • Descriptive statistic
  • Measures of Central Tendency and Dispersion
  • Introduction to data visualisation using Python
  • Univariate graphs, bi-variate graphs, and when to use which
  • Inferential Analytics/Statistics
  • Sample vs Population
  • What is a Hypothesis: Null and alternate hypothesis, test statistic and how to frame a hypothesis for your test
  • Normality and Central Limit theorem: Application to hypothesis testing and inference
  • Types of test statistics
  • How to perform the hypothesis test, p-value, Statistical Power, alpha (confidence), Hypothesis Rejection and Failure to Rejection
  • Impact of sample size on confidence and statistical power
  • Predictive Analytics
  • Introduction to Predictive Analytics and various predictive tasks
  • Introduction to Machine Learning, with examples of how each applies to real-life predictive analytics tasks
  • Deep Dive into supervised (regression and classification) and unsupervised tasks
  • Code examples

Who Should Attend!

  • Jobseekers
  • Aspiring Data Analysts
  • Entrepreneurs
  • Non tech candidates
  • Students in any domain
  • Career change aspirants