Data Science with Python

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Trainig and Internship

Data Science with Python

What you'll learn
  • Data Science Concepts
  • Python
Highlights
  • Certifications – Certificate will be provide after complication of the course.
  • Available –Online / Offline Learning (No recorded lecture / live session of each class).
  • 60 hrs Course..
  • Rich course content for master learning
  • Pay after six demo class.
  • 100% Hands on Course.
  • One MCQ and one case study is compulsory for completion of course..
  • Certificate will generated after successfully completion of the course.
Course Fee
  • Corporate Trainer :25000 INR (18% GST Extra)
  • Students:12000 INR (18% GST Extra)
Prerequisites
  • Mathematics and Statistics.
  • • Basic knowledge of computer

Introduction to Python

Level-1

  • Why to use python ?
  • Python IDE
  • Simple Program in Python
  • Numbers And Math functions
  • Common Errors in Python

Introduction to Python

Level-2

  • Variables & Names
  • String basics
  • Conditional statements
  • Assignment 2
  • Functions
  • For and While

Introduction to Python

Level-3

  • Functions as arguments
  • List,Tuple and Dictionaries
  • List Comprehension
  • File handling
  • Class and Objects

Introduction to Python

Level-4

  • Numpy
  • Pandas
  • List Comprehension
  • Matplotlib
  • Seaborn
  • Ggplote
  • Tensorflow

Module-2

Introduction to Probability Theory

  • Random Variable
  • Binomial Distribution
  • Poisson Distribution
  • Hypergeometric Distribution
  • Methods of Assigning Probabilities
  • Structure of Probability
  • Marginal, Union, Joint, and Conditional Probabilities
  • Addition Laws
  • Multiplication Law
  • Conditional Probability
  • Bayes’ Rule

Module-3

Introduction to Statistics

  • Statistics in Business
  • Data Measurement
  • Descriptive Statistics
  • Measures of Central Tendency:
  • Standard Deviation, Variance, Moments,
  • Covariance and Correlation analysis
  • Descriptive Statistics on the Computer
  • Sampling and Sampling Distribution
  • Distribution of Sample Means, population, and variance
  • Chart and Graphs

Module-4

Making Inferences About Population Parameters.

  • Estimating the Population Mean Using the z Statistic
  • Estimating the Population Mean Using the t Statistic
  • Estimating the Population Proportion.
  • Estimating the Population Variance
  • Estimating Sample Size

Module-5

Statistical Inference and ANOVA

  • Confidence interval estimation: Single population – I
  • Hypothesis Testing- I
  • Errors in Hypothesis Testing
  • Hypothesis Testing: Two sample test
  • ANOVA
  • Post Hoc Analysis (Tukey’s test)
  • Randomize block design (RBD)
  • Two Way ANOVA

Module-6

Regression analysis and Forecasting

  • Linear Regression Model Vs Logistic Regression Model
  • Residual Analysis
  • Using Regression to Develop A Forecasting Trend Line
  • Interpreting the Output
  • Multiple Regression Analysis
  • Confusion matrix and ROC
  • Performance of Logistic Mode
  • Regression Analysis Model Building.

Module-7

Non parametric Statistics

  • Analysis of Categorical Data
  • Chi-Square Test of Independence
  • Chi-Square Goodness of Fit Test

Module-8

Machine Learning

  • Introduction to Machine learning,
  • various type of learning like supervised learning, unsupervised learning, semi supervised learning, Reinforcement learning etc
  • Bias and Variance, Learning parameters
  • Gradient descent
  • Linear regression
  • Nonlinear regression
  • Classification Analysis
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