Data Science with Python 1


8 Weeks

10 Hours/Week






2 or more real time applications.



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.


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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CodeGnan in Students words,

Data Science with Python 2


Learning Java

I’ve tried other training institutes and all I did is quit in the middle. I got nurtured with the basics, they took really good care and even arranged extra classes on personal requests. I get to work in the Real-Time environments and I have successfully deployed 3 projects in Real-Time while I’m still in the middle of the training. The best part is I never thought of quitting.

Data Science with Python 3


Learning Python

I fell in love with the pattern of teaching here. We get to start with hello world all the time but here, nothing is classic and since while training I was working on Real-Time servers, I was a ready to go engineer on the way out of here. They took personal care of me, I got mentors who are happy to answer my calls and finally they gave me a map of what more I can learn by myself and other technologies that I can adapt with my current skillset.

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