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Data Science

Objective: On completing this course, there will be ample opportunities for you to work as a Data Scientist or Data Analyst or AI or ML engineer. You will be able to do some real world concepts like data analysis, Data wrangling, tools of Data science like Python, sqlLite etc. After completion of this course will be able work on real life projects like House Price prediction, stock market analysis etc.

Technical Prerequisite: Knowledge of Python will be required.

System requirement: Min 8 GB RAM, Windows 8 or above, high speed internet connection.

Suitable For: 3rd Yr / 4th Yr B.Tech. / Diploma / MCA / BCA students

Data Science Course Curriculum

Course Duration: 4/6 Weeks & 3/6 Months

Online : Regular Batches / Weekend Batches

Live Project

Certification

Soft Skill Development

Advanced Programs

  • What is Data science
  • Uses of data science
  • Tools of data science
  • Data analysis and predictive modelling
  • Introduction to Python language
  • Data types of Python, numbers, string
  • If, elif, Loops in python
  • Functions and modules in python
  • Lambda function
  • Create class and object in python
  • Creating and accessing strings
  • Indexing and slicing on string
  • Strings methods
  • List and its methods
  • Accessing lists
  • Tuple, set, dictionary and their methods
  • List comprehension and its uses
  • Understanding the uses of various open source libraries
  • Importing various modules with different methods
  • Working with Numpy
  • Numerical operations on numpy array
  • Exploring various use cases of numpy
  • Pandas module and its uses in data analysis
  • Series and Dataframe
  • Need of pre-processing of data
  • Data Wrangling and feature engineering
  • Introduction to sklearn module of python
  • Handling different pre processing technique like missing value impute, explore data, convert from string to number etc
  • Concepts of normalization and standardisation
  • Standardize the dataset using StandardScalar(), MaxMinScalar()
  • Introduction to matplotlib, seaborn
  • Draw different types of graphs using above modules
  • Pie chart, histogram, bar chart, boxplot, count plot etc
  • Introduction to database programming
  • Concepts of RDBMS, table, Column, rows
  • Structured Query language (SQL)
  • DML, DDL
  • Create, insert, delete, update query
  • Concepts of SQLLite
  • Connect anaconda from SQLLite
  • Convert to CSV from database
  • Data analysis
  • What is predictive modelling and machine learning
  • Supervised and Unsupervised machine learning
  • Difference between classification and regression
  • Concepts of train data and test data
  • Types of regression problem, linear regression , polynomial regression
  • Simple Linear Regression and it uses
  • Multiple linear regression
  • What is r2score and RMSE score
  • Linear classification using Logistic regression model
  • Accuracy score, confusion matrix and classification report






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