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Data Science with Python

Data science is a future in many domains now most of the organizations are shifted to python software, Python is a very powerful tool & easy to learn. Data science course can do beginner or professional, we will cover from scratch, To learn this there are no prerequisites, but if you have knowledge about statistics then it will be very useful for you to understand the statistic, if not then we are going to cover all topic of statistic it as well.

This course will enable you to become proficient in the professional world for data Scientist, statistical analysis and data prediction.

Highlights

 
  • Hands on practice
  • Small batch size
  • Industry oriented training
  • Resume preparation
  • Interview preparation
  • Internship opportunity
  • 100% placement support program

Eligibility

Any graduate with prior programming experience can apply.

Duration

Python – 45days (1-hr)

Course program

Online | Classroom
Weekdays | Weekend

Admission Procedure

Fill admission form along with below documents

  •  One photo,
  •  Update resume,
  •  One ID cards
  •  Fee payments

COURSES OUTLINE

Introduction to Data Science and Analytics

  • What is Data Science?
  • What’s the need & why it’s in demand ?
  • What is Data Analytics ?
  • Components of Data Science.
  • Real-life examples & applications.
  • Introduction to different programming languages used for Data Science.

Python Programming 2: Libraries

  • Pandas.
  • Numpy.
  • Sci-kit Learn.
  • Matplot library.
  • Seaborn.

Python Programming 1: Basics

  • Why Python for Data Science?
  • Installing Python.
  • Python IDEs.
  • Python Basic Syntax Data types.
  • Lists.
  • String Manipulation.
  • Conditional Statements.
  • Looping Statements.
  • Dictionaries.
  • Tuples.
  • Functions.
  • Array.

Data Pre-processing & Exploration

  • Extracting data from different sources.
  • Reading XLSX, CSV etc. files.
  • Handling Missing Values.
  • Handling Outliers.
  • Different Data Munging Techniques.
  • One Hot Encoder & Feature Scaling.

Statistics

  • Mean Mode, Median.
  • Random Variable.
  • Probability, Probability Distribution of Random Variables.
  • Type of Random Variables – Based on Scale of Measurement.
  • Normal, Binomial, Poisson Distribution.
  • Standard Normal Distribution and Z-Score.
  • Sampling & Sampling Distribution.
  • Central Limit Theorem.
  • Simulation.
  • Hypothesis and hypothesis Testing.
  • Hypothesis Testing using z-test, t-test.

Machine Learning

  • What is Machine Learning ?.
  • Overview & Terminologies.
  • Difference between AI & ML.
  • Supervised & Unsupervised ML
  • Supervised ML Models.
  • Linear & Logistic Regression.
  • Regression methods, Classification.
  • Sampling & Sampling Distribution.
  • K Nearest Neighbours KNN.
  • Decision Tree, Random Forest.
  • Unsupervised ML Models.
  • K Means Clustering.
  • Under fitting and Overfitting.
  • Confusion Metrix.
  • K-Fold Cross Validation.
  • Regression Evaluation Metrics.
  • Time Series Analysis.
  • Support Vector Machine (SVM).
  • Na’ive Bayes.

Introduction to Data Science and Analytics

  • What is Data Science?
  • What’s the need & why it’s in demand ?
  • What is Data Analytics ?
  • Components of Data Science.
  • Real-life examples & applications.
  • Introduction to different programming languages used for Data Science.

COURSES

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DATA SCIENCE WITH R
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