Udemy – Become a Data Analyst – Job training (beginner to advanced) 2024-1

Apr 18, 2025 - 19:59

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Become a Data Analyst – Job training (beginner to advanced). This course is a practical, job-specific program designed for those interested in entering the field or changing careers. With a training approach based on real-world business needs, this course covers essential data analysis skills from basic to advanced. Participants will learn the basics of data collection and cleaning, statistical analysis, creating analytical reports, and designing intelligent dashboards. The course also includes training in specialized tools such as Python, Pandas, Matplotlib, Seaborn, and Platli. In addition to technical topics, supplementary modules such as AI fundamentals and career guidance (including resume writing, job search, and interviewing) are also offered. The program is designed to prepare students to meet real-world job needs, including predictive analytics, hypothesis testing, and data quality standards.

What you will learn:

Introduction to the field of Data Analysis
How to develop an analytical mindset
Insight into the daily tasks of a Data Analyst
Introduction to Descriptive Statistics
Terms: Sample, Population, Statistic and Parameter
Classification
Prediction
Association
Pattern recognition
Descriptive statistics
Measures of Central Tendency and Dispersion
Introduction to Data Visualization Using Python
Univariate and bivariate graphs and when to use each
Inferential Analytics/Statistics
Sample vs Population
What is a hypothesis: Null and Alternate hypotheses, test statistics, and how to set up a hypothesis for your test
Normality and Central Limit Theorem: Application in Hypothesis Testing and Inference
Types of test statistics
How to perform Hypothesis test, p-value, Statistical Power, alpha (confidence), rejection and non-rejection of hypothesis
The Effect of Sample Size on Confidence and Statistical Power
Predictive Analytics
Introduction to Predictive Analytics and Different Predictive Tasks
An introduction to Machine Learning, with examples of how each is applied to real-world Predictive Analytics tasks.
Complete review of Supervised (Regression and Classification) and Unsupervised tasks
Code examples