6 Months
Skill level
Intermediate - Advanced

Enroll Course

Get an opportunity to become a Data Scientist, Data Engineer, or Data Analyst in just six months. This Data Science oriented Python Training is a specialized simulation-based course, a product for the industry, from the industry. With decades of experience, the industry experts have come together to synchronize with the current market needs. This customized course targets filling the void with the exact talent that current and future markets look for and deserve.

What is Data Science and What is its Utility?

Data science is a computer science field that uses 

  • Scientific Methods
  • Sequential Algorithms
  • Robust Systems 

to extract knowledge and insights from both structured and unstructured data. It also helps to apply this extracted knowledge to derive actionable insights from data across a broad range.

Data Science is the most Trusted Career Option

In the past few years, there have been several career opportunities in this field. Even the drastic impact of the pandemic is not able to decrease its importance. Let’s see why there is a huge scope in this field – 

  • Ever-increasing technological enhancements 
  • Comprehensive growth in the “Internet of Things.”
  • Emerging use of data sources and their management
  • Rapid improvement in Artificial Intelligence & Machine Learning

Why choose Kochiva’s Online Python Training 

Kochiva is a blooming bud of a 25 years old IT firm Kochar Tech. Being a team of IT professionals, we wish to guide students to build a career in Programming. We offer the best online python training with job-oriented skills.

We focus more on Practical Learning. Our six months program has been designed in such a manner that it provides a full-fledged orientation to Python Programming. This course caters to the needs of engineering, data science, and computer students.

Our Job Oriented special Online Python Training Program allows students to learn practical skills. Our Online Python Course aims at providing floor-based skills and robust knowledge to become a data scientist. 

Since the industry itself has established the course, it is taught not by the trainers but by the certified industry experts themselves. For example, our main Python Trainer is himself a patent holder in Data Science.

Our pedagogy relies on practical learning rather than theoretical. Hence, the majority of our learning stands on the LIVE Projects. The faculty is backed by 15-20 years of industry experience and can let experiential learning take command of the program.



Who is it For?

Course Features

  • Complete Course divided into Weekly Modules.
  • Special Offline Doubt Clearing Session for better understanding.
  • One-to-one interaction with the trainers for concept clearing.
  • Guidance from experienced industry leaders to help you out.
  • Total focus on teaching coding skills for experiential learning.
  • 100% Practical Learning with backup theoretical notes and video recordings for more explanation.

Our Training Process

How to Apply?

6 Months course curriculum

  • Introduction to Python Programming.
  • Concept of Data Structure.
  • List, tuple, dictionary, and sets.
  • Important methods of Data Structure and its applications.
  • Introduction to DateTime library and its application in managing data.

 Searching Application and its importance (It’s in Data Science)

  •       Concept and Principle of Stack
  •       Collection library and its important methods
  •       Queue and Deque and its Principle
  •       Implementation, Importance, and Application of Stack.
  •       Search Algorithm

o   Linear Search

o   Binary Search

o   Jump Search.

     Sorting Algorithm

o   Bubble Sort

o   Insertion Sort

o   Merge Sort

o   Quick Sort

  •       Handling Files in Python

o   OS Module – Python Standard Utility Modules

o   File Object Creation

o   Files and Directories

o   Python Input Output Module

o   Functions in Python, itertool package

o   Exceptional Handling – Built in & User defined

o   While, Range, Continue, and Break.

o   Introduction to Exploratory Data Analysis (EDA)

o   Introduction to Feature Engineering and Feature Selection Methods

o   Case Study on Feature Engineering using Python.

o   Classes and its implementation in Python

o   __init__() functions

o   self parameter

o   Classes for daily challenges

o   Measures of Central tendency and its application in understanding insights from Data

o   Probability and its Real-Life Application, Discrete and Continuous Probability distribution.

o   Sampling and its application in Data Science

o   Time Series Analysis

  • Long term and Short term changes in time series
  • ARIMA, SARIMA modeling, and forecasting
  • Exponential Smoothing
  •       Mini Project to cross-check your understanding of Collecting, Preparing, and Analyzing the data.

Note – All Practical Exercises will be based on MS Excel.


  • Introduction to Regression technique and its application.
  • Challenges in Regression Analysis, Evaluation Metrics
  • Logistic Regression/Binary Classification technique.
  • Confusion Matrix and its related metrics for evaluation.
  • Precision, Recall/Sensitivity, Specificity, Accuracy, F1 Score, ROC (Receiver Operating Characteristic) Curve, AUC (Area Under Curve)

Case Studies

  1. Customer Lifetime Value Prediction using a regression technique
  2. Churn Analysis/ Consumer Churn Classification on Insurance Industry data using Logistics Regression.

  • Matplotlib, Seaborn
  • 2D and 3D visualization
  • Tableau – As a complete tool for visualization
  • Basic and Advance charts in Tableau.
  • Sets, Groups, Joining, Blending, and Calculated Field
  • Dashboards in Tableau
  • Story-telling in Tableau.

NoteCase Study  in Tableau

  1. Covid Sentiment Dashboarding based on a monthly tweets analysis
  2. Education Sentiment Dashboarding based on a monthly tweets analysis 

  • Machine Learning Introduction – Supervised and Unsupervised Learning techniques
  • Regression
  • Regularization technique – Lasso and Ridge (L1 and L2)
  • Logistic Modelling
  • Decision Tree for Continuous and Categorical Variable, Entropy, Gini, Chi-square technique
  • Overfitting and Underfitting in Machine Learning
  • Ensemble technique for solving Overfitting, Random Forest

  • Understanding Hyperparameter for Supervised Machine Learning technique.
  • Tools for Hyperparameter tuning in Sklearn
  • Class Imbalancing and its Handling in Python.
  • Knn (k nearest neighbor)

  • Boosting techniques, AdaBoost
  • Gradient Boost
  • XG Boost
  • K-means
  • Hierarchical and Agglomerative Clustering 

  • Introduction to Tensorflow library
  • Basic Mathematical operations on tensor objects
  • Neural Network, Activation function, Loss function, and Weights
  • Forward propagation, Backward Propagation, Gradient Descent function
  • Introduction to Convolution Neural Network (CNN)
  • Layer, Mapping, Padding, Pooling, and resizing the data for machine learning

  • CNN 
  • RNN
  • LSTnet 


  1. Deep Learning Project 1 – Image Classification
  2. Deep Learning Project 2 – Time Series Analysis using LSTM
  3. Deep Learning Project 3 – Face Recognition based on some Public Data Source.

  • There will be open platform discussion with our Data Science experts for deciding self-motivated projects.
  • Each Student will be closely monitored by our experts and will be open to their project discussion and guidance as and when needed.


Blockchain technology and its scope in the market.

  • AWS (Amazon Web Services) for Data Engineering, Machine Learning.

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