DATA SCIENCE WITH PYTHON TRAINING IN VIJAYAWADA

Data Science with Python

Data is treated as the new currency in the world. Every day there are more than 2.5 quintillion bytes of data generated which needs to be sorted and analyzed to be used later.  The volume of data is growing exponentially and it results in a vast demand for data scientists. Data science training is helping individuals take advantage of this vast demand. Companies are spending anywhere between hundreds of thousands of dollars to billions of dollars on software and personnel to be able to analyze the available data to get an advantage over their competitors as well as to increase their market share.

Why Data Science with Python training is required?

Python is one of the most flexible coding platforms which can be used for a huge range of activities. It is open-source software. Data science with Python uses the features and flexibility of Python to carry out a number of programming to analyze data. Data science training with Python will help you to understand and execute the concept of Machine Learning. Python is easy to use and understand, simple yet powerful, and provides a platform for innovation as it can be used in a wide range of contexts. An alternative to this is data science with R. However, Python has been proven to be better than R which is why data science with Python is more relevant than data science with R. Codegnan is a leading Institute which provides Python backed data science course in Vijayawada.

Advantages of Data Science with Python

You will gain a better understanding of business analytics;
You will be able to analyze the available data for a wide range of activities such as market research, product recommendation, and much more;
You will also learn to use Machine Learning and be able to write supervised as well as unsupervised programs;
Data science training but also help you to formulate statements for testing hypothesis through parametric and non-parametric tests;
You will also be able to use your knowledge of Datascience to measure the correlation coefficient of the data;
Moreover, you will also be able to extract text from web pages through text mining;
Being able to analyze the data will also enable you to forecast a trend or result of an event;
You will also be able to access several Datascience libraries, such as Pandas, Numpy, and Spicy, which will help you to study, practice, and operate with an example dataset;
Our course also includes learning how to use Pandas, an open-source library, to store, manage, interpret, and conceive datasets; and many more such advantages

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

We provide a data science course in Vijayawada using the Python programming language. Our team has extensive knowledge and years of experience in developing Datascience programs. Our 80 hours course is divided into 9 parts to help you better understand the various aspects involved in data science programming. We are a premier institute which aims to provide you with unmatched knowledge and training to help you with real-time experiences.

CURRICULUM

Course Syllabus

Part 1
Introduction to Python.
Who is using Python today?
Installation and setting up environment.
Basic syntax.
Built in data types.

Part 2
Basic Operators.
Decision making.
Loops.
Numbers.
Strings.
Lists.

Part 3
Tuples.
Sets.
Dictionary.
Functions.
Modules.

Part 4
Packages.
Files.
Exceptions.
Classes.
Regular Expressions.

Part 5
Database Access.
Networking.
Sending Email.
Building Microservices.
Working with git.

Part 6
Numpy.
Multi-dimensional array.
Files with arrays.
Linear Algebra.
Array Manipulation.
Structured and record arrays.

Part 7
Pandas.
Series.
Data Frames.
Reading and Writing Data in Text Format.

Part 8
Interacting with web APIs.
Interacting with Databases.
Handling missing data.
Combining and merging datasets.

Part 9
Matplotlib.
Plotting and visualizing data.
Data aggregation.
Time series.
Time zones.
Sickit learn.

Part 10
Introduction to Machine Learning
How do machines learn
Types of machine learning
Supervised learning
Unsupervised learning
Reinforcement learning

Applications of machine learning


Part 11
Selecting a model
Training a model
Classification
Regression
Clustering
Performance of a model

Part 12
What is a feature
Feature construction
Feature extraction
Feature selection

Part 13
Supervised learning classification.
Bayes Theorem
Naïve Bayes Classifier
K-Nearest Neighbour (KNN)
Decision Tree.
Random Forest Model
Support Vector Machines

Part 14
Supervised learning regression.
Simple linear regression.
Multiple linear regression.
Problems in regression analysis.
Polynomial regression model.
Logistic Regression.

Part 15
Unsupervised vs Supervised learning.
Applications of Unsupervised Learning
Clustering

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