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Data Science Training

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Expert-Led Sessions

Witness the excellence of the topic through the people who actually are the certified experts of the matter. Learn through experiences and not the books.

LIVE Classes

Forget the monotony of the recorded self paced sessions, when you can learn Data Science through LIVE, interactive classes, where the chances of having a doubt left are bare minimum.

Placement Assistance

Forget wondering on 'What Next'? We have the solution. Post Data Science Course completion, we would also be helping you with placements in Top Companies at Top Packages.


Data Structures in Python
a. Introduction to Python Programming.
b. Concept of Data Structure.
c. List, tuple, dictionary, and sets.
d. Important methods of Data Structure and its applications.
e. Introduction to DateTime library and its application in managing data.

Concept of Stack and Queue
Searching Application and its importance (It’s in Data Science)

a. Concept and Principle of Stack
b. Collection library and its important methods
c. Queue and Deque and its Principle
d. Implementation, Importance, and Application of Stack.
e. Search Algorithm
i. Linear Search
ii. Binary Search
iii. Jump Search.

Sorting Algorithms and its applications
a. Sorting Algorithm
b. Bubble Sort
c. Insertion Sort
d. Merge Sort
e. Quick Sort
f. Handling Files in Python
i.OS Module – Python Standard Utility Modules
ii. File Object Creation
iii. Files and Directories
iv. Python Input Output Module

Introduction to Functions and its applications in Data Science
a. Functions in Python, itertool package
b. Exceptional Handling – Built in & User defined

Control statements in Python
a. While, Range, Continue, and Break.
b. Introduction to Exploratory Data Analysis (EDA)
c. Introduction to Feature Engineering and Feature Selection Methods
d. Case Study on Feature Engineering using Python.

Classes in Python
a. Classes and its implementation in Python
b. __init__() functions
c. self parameter
d. Classes for daily challenges

Statistics I – Basic statistics for Machine Learning and Analytics
a. Measures of Central tendency and its application in understanding insights from Data
b. Probability and its Real-Life Application, Discrete and Continuous Probability distribution.
c. Sampling and its application in Data Science
d. Time Series Analysis
e. Long term and Short term changes in time series
f. ARIMA, SARIMA modelling, and forecasting
g. Exponential Smoothing
h. Mini Project to cross-check your understanding of Collecting, Preparing, and Analyzing the data.

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

Statistics II – Advance statistical Technique for Machine Learning
a. Introduction to Regression technique and its application.
b. Challenges in Regression Analysis, Evaluation Metrics
c. Logistic Regression/Binary Classification technique.
d. Confusion Matrix and its related metrics for evaluation.
e. Precision, Recall/Sensitivity, Specificity, Accuracy, F1 Score, ROC (Receiver Operating Characteristic) Curve, AUC (Area Under Curve)

Case Studies

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

Introduction to Tableau and Visualization Techniques in Python
a. Matplotlib, Seaborn
b. 2D and 3D visualization
c. Tableau – As a complete tool for visualization
d. Basic and Advance charts in Tableau.
d. Sets, Groups, Joining, Blending, and Calculated Field
e. Dashboards in Tableau
f. Story-telling in Tableau.

NoteCase Study in Tableau

a. Covid Sentiment Dashboarding based on a monthly tweets analysis
b. Education Sentiment Dashboarding based on a monthly tweets analysis

Predictive Analytics and Data Analytics using Python
a. Machine Learning Introduction – Supervised and Unsupervised Learning techniques
b. Regression
c. Regularization technique – Lasso and Ridge (L1 and L2)
d. Logistic Modelling
e. Decision Tree for Continuous and Categorical Variable, Entropy, Gini, Chi-square technique
f. Overfitting and Underfitting in Machine Learning
e. Ensemble technique for solving Overfitting, Random Forest

Hyperparameter tuning for Machine Learning Models
a. Understanding Hyperparameter for Supervised Machine Learning technique.
b. Tools for Hyperparameter tuning in Sklearn
c. Class Imbalancing and its Handling in Python.
d. Knn (k nearest neighbour)

Boosting and Unsupervised Learning Technique
a. Boosting techniques, AdaBoost
b. Gradient Boost
c. XG Boost
d. K-means
e. Hierarchical and Agglomerative Clustering

Deep Learning using Tensorflow
a. Introduction to Tensorflow library
b. Basic Mathematical operations on tensor objects
c. Neural Network, Activation function, Loss function, and Weights
d. Forward propagation, Backward Propagation, Gradient Descent function
e. Introduction to Convolution Neural Network (CNN)
f. Layer, Mapping, Padding, Pooling, and resizing the data for machine learning

Neural Networks
a. Convolution Neural Network (CNN)
b. Recurrent Neural Network(RNN)
c. Long Short Term Memory (LSTM)

Dedicated Project based on Deep Learning Technique
a. Deep Learning Project 1 – Image Classification
b. Deep Learning Project 2 – Time Series Analysis using LSTM
c. Deep Learning Project 3 – Face Recognition based on some Public Data Sources.

Self Driven Projects
a. There will be open platform discussion with our Data Science experts for deciding self-motivated projects.
b. 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.

Cloud Technology and its applications in Data Science
a. AWS (Amazon Web Services) for Data Engineering, Machine Learning.


Easy Placements

With career oriented Data Science course at Kochiva, we make sure that you are the most suitable employee for any company.

Hike Opportunities

With all the trainings targetted towards practicality, you would definitely be 1 out of many and hence, the first preference for hike.

Developed Personality

Courses at Kochiva not only make you technically strong, but make your personality a literal charm.

Admission Process




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Whether you’re just starting or have been using it for years, it never hurts to learn and remember where you started. I have learnt so much from this one month course. This course helps me a lot to improve my communication skill, helps me to groom myself.

Awesome experience and good to interact with the wonderful faculties of Kochiva.

Chetan Akash

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