With our Data Analytics Certification Training by Techpratham, you'll learn how to turn raw data into useful information. Find out how to use tools, techniques, and frameworks to make business decisions based on data.
Level
Advanced
Duration
8 Weeks



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This course covers all the basics of data analytics, including how to collect, clean, visualize, and model data to make predictions. You will learn how to use industry tools like Excel, SQL, Python, and Tableau/Power BI by doing them yourself. Find out how to read complicated datasets, make dashboards that matter, and use analytics to solve real-world business problems. After this training, you'll be ready for jobs in business intelligence and data analysis.
Working professional who is carrying more then 10 years of industry experience.
Access to updated presentation decks shared during live training sessions.
E-book provided by TechPratham. All rights reserved.
Module-wise assignments and MCQs provided for practice.
Daily Session would be recorded and shared to the candidate.
Live projects will be provided for hands-on practice.
Expert-guided resume building with industry-focused content support.
Comprehensive interview preparation with real-time scenario practice.
Introduction to Data Analytics
Understand the fundamentals of data analytics and its business applications.
Data Collection & Data Sources
Recognize how to collect data from various sources, both structured and unstructured.
Data Preparation & Cleaning
To ensure accurate analysis, learn how to clean, transform, and preprocess data.
Exploratory Data Analysis (EDA)
Find trends, identify abnormalities, and provide a statistical and visual summary of datasets.
Data Visualization
Develop your ability to convey ideas through captivating and understandable images.
Statistical Analysis for Data Analytics
Utilize statistical methods for data analysis and interpretation.
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Sales Performance Dashboard
Make an interactive dashboard in Power BI or Tableau to look at sales performance by region and product. The project is all about cleaning up data, looking for trends, and setting key performance indicators (KPIs) so that management can make decisions based on data.
Customer Churn Prediction
Use Python and machine learning to figure out how likely it is that a telecom company will lose customers. This project includes cleaning up the data, making new features, and making predictive models to find customers who are at risk.
Market Basket Analysis
Use association rule mining on data from retail transactions to find patterns in how people buy things. This project helps with analyzing customer behavior, making recommendations, and cross-selling.

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