### Data Science with Python  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
• Multiplication Law
• Conditional Probability
• Bayes’ Rule ### Module-3

Introduction to Statistics

• 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

• 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