A data analytics course is designed to teach students or aspiring data analysts how to collect, process, analyze, and interpret data to extract valuab...Read more

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Course Highlights


A data analytics course is designed to teach students or aspiring data analysts how to collect, process, analyze, and interpret data to extract valuable insights and support data-driven decision-making. These courses typically cover a range of topics in data collection, data cleaning, statistical analysis, data visualization, and reporting. Here's an overview of what you might expect in a data analytics course. Techniques for exploring data to identify patterns, outliers, and relationships.


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Course Curriculum

  • Why do Organizations needs a Data Analyst
  • Introduction to Data analysis & Evolution of Data Science
  • Difference between Data analyst & Data Scientist
  • Roles & Responsibilities of Data Analyst
  • Different types of Data
  • What is exploratory data analysis?
  • 7 steps in EDA?
  • How to perform EDA on Real time Dataset
  • Getting Started With Tableau
  • Working with Tableau
  • Deep Diving with Data and Connections
  • Creating Charts
  • Adding Calculations to Your Workbook
  • Mapping Data in Tableau
  • Dashboards and Stories
  • Visualizations for an Audience
  • Bar plots
  • Basic Matplotlib concepts
  • Subplots
  • Histograms
  • Advanced Matplotlib concepts
  • Data Visualization
  • Correlation and Regression
  • Data Ethics and Privacy
  • Critical Thinking
  • SQL Fundamentals
  • Excel Data Analysis
  • Segmentation and Clustering
  • Data Wrangling
  • Statistical Software (e.g.
  • R
  • Python
  • Jupyter
  • Pandas)
  • Data Collection and Preparation
  • Data Analytics Process
  • Segmentation and Clustering
  • Data Storytelling
  • Data Wrangling
  • Exploratory Data Analysis (EDA)
  • Data Exploration and Cleaning
  • Segmentation and Clustering
  • Data Validation and Quality Control
  • Data-driven Decision Making

Professional Certificate

Beginner level

No previous experience necessary

Course Session

30 Days 1 Hours per day

Flexible schedule

Learn at your own pace

Course Key Features

Students learn how to gather data from various sources, including databases, spreadsheets, web scraping, and APIs

Creating clear and concise reports and dashboards to communicate findings to stakeholders

Skills Covered
  • Data Collection
  • SQL
  • Critical Thinking
  • Communication
Job Roles
  • Data Analyst
  • Business Analyst
  • Database Administrator (DBA)


The training is designed to describe the main goals and objectives of the training, such as enhancing skills, improving knowledge, etc.
This training is intended for specify the target audience, such as beginners, professionals, specific job roles, etc.
List any required prerequisites, such as prior knowledge, experience, or skills that participants should have before taking the training.

Interview Question

The Data Analytics process typically includes: Data Collection: Gathering relevant data from various sources. Data Cleaning: Removing inconsistencies, errors, and duplicates from the data. Data Transformation: Converting and organizing data into a usable format. Data Exploration: Exploring data using summary statistics, visualizations, and initial insights. Data Analysis: Applying statistical and analytical methods to draw conclusions and insights.
There are several types of data analysis: Descriptive Analysis: Summarizing and describing data to understand its basic characteristics. Diagnostic Analysis: Analyzing data to determine the reasons behind specific outcomes. Predictive Analysis: Using historical data to make predictions about future outcomes. Prescriptive Analysis: Providing recommendations and actions based on analysis results.
Handling missing data can involve techniques such as: Removing rows with missing values (if feasible). Imputing missing values using mean, median, or more advanced methods. Using machine learning algorithms to predict missing values.

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