Data Analytics With Python Full Course | Data Analysis With Python | Data Analysis | Simplilearn
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In this data analytics with Python full course video, you'll learn to analyze and visualize data using Python libraries. Data analytics plays a vital role in every company for making crucial decisions and improving the business. You will see the different applications of Data Analytics and the various types of Data Analytics. You will deep dive into learning Data Analytics using NumPy, Pandas, Matplotlib, Seaborn, and Bokeh.
The below topics are covered in this video:
0:00:00 - 0:01:22 Introduction
0:01:22 - 01:22:19 Data Analysis using NumPy
01:22:19 - 02:14:42 Data Manipulation with Pandas
02:14:42 - 03:16:05 Matplotlib Data Visualization
03:16:05 - 03:56:07 Seaborn Data Visualization
03:56:07 - 04:51:30 Bokeh Data Visualization
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What is Data Analysis in Python?
Data analysis is a process of examining, cleaning, modifying, and modeling data with the purpose of identifying useful information and hidden trends. It helps you understand the data better and make useful decisions.
➡️ About Data Analyst Master’s Program
This online data analyst course will transform you into a data analytics expert. In this certification course, you will learn the latest analytics tools and techniques, how to work with SQL, the languages of R and Python, the art of creating data visualizations, and how to apply statistics and predictive analytics in a business environment.
✅ Key Features
Simplilearn's JobAssist helps you get noticed by top hiring companies
Industry-recognized Data Analyst Master’s certificate from Simplilearn
Dedicated live sessions by faculty of industry experts
Masterclasses from IBM experts
Industry-recognized IBM certifications for IBM courses
Ask-Me-Anything (AMA) sessions with IBM leadership
Capstone from 3 domains and 20+ projects
Exclusive hackathons conducted by IBM
Lifetime access to self-paced learning content
Program crafted to initiate your journey as a Data Analyst
✅ Skills Covered
Data Analytics
Statistical Analysis using Excel
Data Analysis using Python and R
Data Visualization Tableau and Power BI
Linear and logistic regression modules
Clustering using KMeans
Supervised Learning
Unsupervised Learning
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