Master In Data Science
Data is the New Fuel. analyzing data is need of time. This course covers all the important areas such as statistics, python, Machine Learning, SQL, PowerBi, etc. Upgrade yourself and be the part of the next revolution.
Modules
8
Duration
150 Hours
Register By
January 29th 2023
Modes
, Online,
Course Fee :
Live Online Classes
20000 /- INR
Industry Oriented Curriculum
An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company
15
Case Studies
24
Assignments
10
Quizes
7
Tools
7
Capstone Projects
What you will learn from this course :
After completing a hands-on Data Science training program, individuals can expect to have developed a range of practical skills and knowledge, including:
Familiarity with data science tools and libraries, such as Python's Pandas, Numpy and Scikit-Learn libraries.
Experience in cleaning, pre-processing, and visualizing data using Python or R and Power-BI also.
Knowledge of Statistical Modelling and Machine Learning concepts and their applications.
Understanding of the data science process and best practices for working with data.
Experience in implementing various Machine Learning Algorithms and techniques on real-world datasets.
Understanding of how to evaluate and interpret the results of a data science model.
It is worth noting that, hands-on training is considered as the best way to learn data science, as it allows the learners to apply the concepts in real-world scenarios and gain practical experience in solving real-world problems.
Master In Data Science with Curriculum
Prerequisites for this course
A list of topics that are good to have before you hit the ground running with this course
No Prerequisite
Statistics
10 Hrs
Data Science and Data Analytics • What is data? • Type of data • What is Data science? • What is data analytics? • Difference between data science and data analytics • Tools and Technologies
Introduction to Statistics • Descriptive statistics • Inferential statistics • Importance in DS and DA
Data Visualization • Variable analysis • Visualization of Continuous data • Visualization of Discrete data • Interpretation of graphs
Python Programming
36 Hrs
Introduction
Variables
Operators
Control structure (conditional/ iterative)
Datatypes in python (string, lists, dictionary, tuples, sets)
Functions in python
File Handling
Modules in python
OOPs in python
Exception handling
Multithreading
Analysis with python
20 Hrs
NumPy • Creation of NumPy array • Built-in functions and properties of num py array • Slicing and indexing • Masking and broadcasting operations • Aggregate Functions • Vectorization Pandas • Series • Data Frame • Data Frame properties • Data Frame indexing and slicing • Reading data from various sources • Data frame functions • Pandas functions • Filter Data Visualization libraries (matplotlib/seaborn) • Introduction to Matplotlib and Seaborn • Properties of plots • Line plot • Histogram / Dist plot • Bar plot / Count Plot • Pie chart • Heat map • Scatter plot • Box Plot
Machine Learning
48 Hrs
Introduction • Introduction to ML • Applications of ML • Difference between supervised and unsupervised learning • Regression vs classification
EDA : Exploratory data Analysis • Fundamental EDA • Steps in EDA • Handling missing values • Handling outliers • Handling skewness of data • Normalization, scaling • Encoding Supervised Learnings Regression • Assumptions of linear regression • Simple linear regression • Error calculation (MAE, MSE, RMSE) • Evaluation metrics(r2_score) • Multiple linear regression • Polynomial linear regression
Regularization • Overfitting / underfitting • Bias and variance • Trade-off • Ridge and lasso regularization • Cross validation (kfold method)
Classification : • Logistic Regression • KNN • Support Vector Machine (SVM) • Decision Tree Classifier • Ensemble Learnings (Bagging and Boosting algorithms) Unsupervised Learning • Kmeans • Hierarchical clustering • PCA • Association rules
MODEL DEPLOYMENT
04 Hrs
Introduction
Deployment Process
SQL: Structured Query Language
12 Hrs
• Introduction • DDL DML commands • DQL • Built-in SQL functions (Date, string, aggregate) • Joins and subquery
DATA VISUALIZATION TOOL (Power Bi)
25 Hrs
• Desktop layout • Builtin aggregations • Reports and dashboards • DAX commands and functions
System Requirments
Common system requirments that you will need to have before moving ahead with this course
- Atleast 4GB RAM
- Stable Internet Connection
- OS : Linux / Windows / Mac
On Completion
On Successfull Completion of
Traning on
Master In Data Science
Trainee will get 2 Certificates:1. “Certificate of Achievement”
2. “Certificate of Internship”
Note : Internship Certificate will be awarded only after Successfull Completion of Capstone Projects
