DATA ANALYSIS WITH PYTHON TRAINING IN VIJAYAWADA

Data Analysis with Python

This program delivers you from the basics of Python to exploring several kinds of data. You will discover how to adjust data for analysis and make simplistic statistical critiques, produce important data visualizations, foretell expected drifts from data.

Why Data Analysis with Python training is required?

Python is a trendy tool concerning data analysis. In recent years, many libraries have approached capability, deducting users to take advantage of the excellence, versatility, and production of Python outwardly losing the functionality those records have expanded over the years.

Advantages of Data Analysis with Python

 Data Analysis libraries will encourage you to study and practice Pandas, Scipy and Numpy libraries to operate with an example dataset.
We will include pandas, an open-source library, including how to use it to store, manage, interpret, and conceive datasets.
Then we will continue to a different open-source library, sci-kit learn and will practice some of its machine learning algorithms to create forward models and make predictions.

Why CodeGnan to be chosen to learn Data Analysis with Python training in Vijayawada?

We as a team of real-time industrial experience with a lot of knowledge in developing Data Analysis using python programming (7+ years) will ensure that we will deliver our best in Data Analysis with Python Training in Vijayawada, and we believe that no one matches us in this context.

CURRICULUM

Course Syllabus

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Part 1
Introduction to Python.
Who is using Python today?
Installation and setting up environment.
Basic syntax.
Built in data types.
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Part 2
Basic Operators.
Decision making.
Loops.
Numbers.
Strings.
Lists.
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Part 3
Tuples.
Sets.
Dictionary.
Functions.
Modules.
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Part 4
Packages.
Files.
Exceptions.
Classes.
Regular Expressions.
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Part 5
Database Access.
Networking.
Sending Email.
Building Microservices.
Working with git.
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Part 6
Numpy.
Multi-dimensional array.
Files with arrays.
Linear Algebra.
Array Manipulation.
Structured and record arrays.
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Part 7
Pandas.
Series.
Data Frames.
Reading and Writing Data in Text Format.
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Part 8
Interacting with web APIs.
Interacting with Databases.
Handling missing data.
Combining and merging datasets.
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Part 9
Matplotlib.
Plotting and visualizing data.
Data aggregation.
Time series.
Time zones.
Sickit learn.

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