Data Science | Kochiva Data Science - Kochiva

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Curriculum

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.

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

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

a. Functions in Python, itertool package
b. Exceptional Handling – Built in & User defined

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.

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

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.

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

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

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

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)

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

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

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

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.

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.

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

Courses Details

Duration

6+ Months

Placement Assistance

Yes

Mode

Online

Certification

Yes

How does Kochiva’s Data Science Course Transform you?

Kochiva’s Data Science course is specifically designed for busy people, even full time job holders.

Live Classes with Experts

Assignments and Extra Classes

Interview Preparations

Frequently Asked Questions?

If you are interested in our course, you can simply fill up the enrollment form available on the website. Very soon, Kochiva’s Admission Counselling Team shall get in touch with you.

Anyone who has the fire to code and basic knowledge of how IT works can go ahead with the programme. Our Programmes are beginner-friendly.

One needs to have an active internet connection with at least 1 Mbps download speed. One also needs to have a working laptop/desktop pc with the following specifications. 1. 1 GHz Processor 2. 4 GB RAM 3. 120 GB Hard Drive 4. Microphone 5. WebCam

Before enrolment, Kochiva’s Admission Ready Test (KART) is organised which includes a technical round, aptitude round and a one-to-one general interview. Kochiva makes sure that a person is sound enough to handle the rigorous training and is able to understand the future proceedings of the course.