DATA SCIENCE WITH PYTHON

USD 200.00 USD 150.00

25% Discount

The first class is FREE upon registration.

NISHANT SIDANA

Course Language: EN (English)

REGISTER FOR FREE
This course includes:

44h 0m live online session

Certificate of completion

Level:
Beginner
Target Audience:
  • Engineering, MBA, Economics, Any Graduate
Course objective:
  • Upskill participants with latest data science skills which will make them industry ready & help them with Data Science & AI roles in industry.
Course prerequisites:
  • A laptop with 64GB RAM
Video Recording Available:
  •  No

Description :

Module 1: Python Basics: It will help learn the tool, Python to be used for working with data

·       Introduction to Python

·       OOP: Object & Class

·       Serialization: Pickle Library

·       Variables

·       Lists

·       Tuples

·       Dictionary

·       Sets

·       List and Dictionary Comprehensions

·       Conditional Statements (If, If-else, elif)

·       Loops (For, While)

·       Functions

·       Lambda Function

·       Apply Function

Class Exercises

 

Module 2: Python NUMPY Library: It is used to perform a wide variety of mathematical operations on arrays

·       Array Characteristics

·       Array Creation (arrange, linspace, flatten)

·       Array Indexing (Slicing)

·       Array Manipulation

·       Reshape

·       Concatenate

·       Append

·       Insert

·       Delete

·       Transpose

Class Exercises

 

Module 3: Python PANDAS Library: It is used for data manipulation, data cleaning, data analysis

·       Series

·       Data Frames

·       Reading csv file

·       Sub setting / Filtering / Slicing Data

·       Dropping rows & columns

·       Adding/Deleting columns

·       Binning

·       Renaming columns or rows

·       Sorting

·       Data type conversions

·       Handling duplicates /missing

·       Broadcasting

·       Group by Function

·       Map Function

·       Visualization (bar graph, histogram, box plot)

·       Merging (Inner, Left, Right, Outer)

·       EDA

Class Exercises

 

Module 4: Python MATPLOTLIB Library: Data Visualization part 1

·       Bar Plot

·       Stacked Bar Plot

·       Histogram

·       Line Chart

·       Box plot

·       Pie-Chart

Class Exercises

 

Module 5: Python SEABORN Library: Data Visualization part 2

·       Bar Plot

·       Histogram

·       Pairwise Plots: Joint Plot, Pair Plot

·       Categorical Scatter Plot: Strip-plot, Swarm-plot

·       Box-Plot

·       Violin Plot

·       Cat Plot

·       Facet Grid

·       Pair Grid

·       Line Plot

Class Exercises

 

Module 6: Basic Statistics: For business analysis

·       Type of Data

·       Statistics

·       Type of Statistics

·       Descriptive Statistics

·       Mean, Median, Mode (Measures of Central Tendency)

·       Standard Deviation, Variance (Measures of Dispersion)

·       Normal Distribution

·       Standard Normal Distribution

·       Standard Error

·       Sampling

·       Probability

Class Exercises

 

Module 7: Advance Statistics: For business analysis

·       Confidence Interval

·       T-Test & Z-Test

·       P-value

·       Hypothesis Testing

·       Type I Error & Type II Error

·       Chi-Square Test

·       ANOVA

·       Covariance

·       Correlation

Class Exercises

 

Module 8: Machine Learning

·       Supervised

·       Unsupervised

 

Module 9: Supervised Machine Learning: Linear Regression (Solve business problems where we have to predict a value)

·       Introduction

·       Assumptions (Linearity, Heteroskedasticity, Multivariate Normality, etc.)

·       Data Preparation (Outlier Treatment, Missing Value Imputation)

·       Building Linear Regression Model

·       Understanding model metrics (p-value, R-square/Adjusted R-square etc.)

·       Multicollinearity (VIF)

·       Model Validation (MAPE, RMSE)

Case study

 

Module 10: Supervised Machine Learning: Logistic Regression (Used for binary classification business problems)

·       Introduction

·       Linear Regression Vs. Logistic Regression

·       Data Preparation (Outlier Treatment, Missing Value Imputation, Dummy Variable Creation)

·       Building Logistic Regression Model

·       Understanding model metrics (p-value)

·       Multicollinearity (VIF)

·       Model Validation (Confusion Matrix, ROC curve, AUC, etc.)

Case study

 

Module 11: Supervised Machine Learning: Decision Tress (Used for multi-class classification business problems & regression business problems)

·       Introduction

·       Types

·       Entropy, Gini Index, Chi-Square

·       Overfitting

·       Pruning

·       Cross – Validation

Case study

 

Module 12: Supervised Machine Learning: Ensemble (Used for multi-class classification business problems & regression business problems)

·       Introduction

·       Bagging

o   Random forest

·       Boosting

o   Gradient Boosting Machines (GBM)

Case study

 

Module 13: Supervised Machine Learning: KNN (Used for multi-class classification business problems & regression business problems)

·       Introduction

·       Working of KNN

·       Optimal value of K

Case study

 

Module 14: Unsupervised Machine Learning: Clustering (Used for segmenting data points into different groups)

·       Introduction

·       K -Means Clustering

·       Cluster Evaluation and Profiling

Case study

 

Module 15: Unsupervised Machine Learning: PCA (Used for segmenting data points into different groups)

·       Introduction

·       Curse of dimensionality

·       Process of working

Case study

 

Module 16: Unsupervised Machine Learning: Isolation Forest (Used for anomaly detection business problems)

·       Introduction

·       Contamination Factor

Case study

 

Module 17: Time Series Forecasting: Used for inventory planning or forecasting business problems

·       Introduction

·       Time Series Components: Trend, Seasonality, Cyclicity

·       Smoothening Techniques– Moving Averages, Exponential

·       ARIMA

·       Accuracy

Case study

 

Module 18: Text Analytics: Used for text mining business problems working with unstructured data

·       Introduction

·       Text Pre-processing

o   Noise Removal

o   Lemmatization

o   Stemming

o   Feature Engineering on Text Data

o   Bag of words

o   TF-IDF

Case study

 

Module 19: AI: Deep Learning, Keras

·       Introduction: Deep Learning

·       Deep Learning vs Machine learning

·       Neural Networks

·       Activation Functions, hidden layers, hidden units

·       Backpropagation

·       Vanishing Gradient Problem

·       Exploding Gradient Problem

·       Perceptron & Multi-layer Perceptron

Case study

 

Module 20: Model Deployment: Using model for predicting output on new input values

·       Flask

Case study

John Doe

NISHANT SIDANA

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India

Senior Manager-Data Science, Freelance Data Science Trainer (R,SAS,PYTHON,ML,NLP,AI)(3000+ individuals), Mentor

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Course Content : Expand all sections

  • 34 lectures
  • 44h 0m total length

·       Introduction to Python

·       OOP: Object & Class

·       Variables

·       Lists

·       Tuples

·       Dictionary

·       Sets

·       List and Dictionary Comprehensions

·       Conditional Statements (If, If-else, elif)

·       Loops (For, While)

·       Functions

·       Lambda Function

·       Apply Function

Class Exercises

·       Lists

·       Tuples

·       Dictionary

·       Sets

·       List and Dictionary Comprehensions

·       Conditional Statements (If, If-else, elif)

·       Loops (For, While)

·       Functions

·       Lambda Function

·       Apply Function

Class Exercises

·       Array Characteristics

·       Array Creation (arrange, linspace, flatten)

·       Array Indexing (Slicing)

·       Array Manipulation

·       Reshape

·       Concatenate

·       Append

·       Insert

·       Delete

·       Transpose

Class Exercises

 

·       Series

·       Data Frames

·       Reading csv file

·       Sub setting / Filtering / Slicing Data

·       Dropping rows & columns

·       Adding/Deleting columns

·       Array Characteristics

·       Array Creation (arrange, linspace, flatten)

·       Array Indexing (Slicing)

·       Array Manipulation

·       Reshape

·       Concatenate

·       Append

·       Insert

·       Delete

·       Transpose

Class Exercises

 

·       Series

·       Data Frames

·       Reading csv file

·       Sub setting / Filtering / Slicing Data

·       Dropping rows & columns

·       Adding/Deleting columns

·       Binning

·       Renaming columns or rows

·       Sorting

·       Data type conversions

·       Handling duplicates /missing

·       Broadcasting

·       Group by Function

·       Map Function

·       Visualization (bar graph, histogram, box plot)

·       Merging (Inner, Left, Right, Outer)

·       EDA

Class Exercises

·       Bar Plot

·       Stacked Bar Plot

·       Histogram

·       Line Chart

·       Box plot

·       Pie-Chart

Class Exercises

·       Bar Plot

·       Histogram

·       Pairwise Plots: Joint Plot, Pair Plot

·       Categorical Scatter Plot: Strip-plot, Swarm-plot

·       Box-Plot

·       Violin Plot

·       Cat Plot

·       Facet Grid

·       Pair Grid

·       Line Plot

Class Exercises

·       Type of Data

·       Statistics

·       Type of Statistics

·       Descriptive Statistics

·       Mean, Median, Mode (Measures of Central Tendency)

·       Standard Deviation, Variance (Measures of Dispersion)

·       Normal Distribution

·       Standard Normal Distribution

·       Standard Error

·       Sampling

·       Probability

Class Exercises

·       Confidence Interval

·       T-Test & Z-Test

·       P-value

·       Hypothesis Testing

·       Type I Error & Type II Error

·       Chi-Square Test

·       ANOVA

·       Covariance

·       Correlation

Class Exercises

·       Type of Data

·       Statistics

·       Type of Statistics

·       Descriptive Statistics

·       Mean, Median, Mode (Measures of Central Tendency)

·       Standard Deviation, Variance (Measures of Dispersion)

·       Normal Distribution

·       Standard Normal Distribution

·       Standard Error

·       Sampling

·       Probability

Class Exercises

·       Confidence Interval

·       T-Test & Z-Test

·       P-value

·       Hypothesis Testing

·       Type I Error & Type II Error

·       Chi-Square Test

·       ANOVA

·       Covariance

·       Correlation

Class Exercises

·       Introduction

·       Assumptions (Linearity, Heteroskedasticity, Multivariate Normality, etc.)

·       Data Preparation (Outlier Treatment, Missing Value Imputation)

·       Building Linear Regression Model

·       Understanding model metrics (p-value, R-square/Adjusted R-square etc.)

·       Multicollinearity (VIF)

·       Model Validation (MAPE, RMSE)

Case study

·       Introduction

·       Assumptions (Linearity, Heteroskedasticity, Multivariate Normality, etc.)

·       Data Preparation (Outlier Treatment, Missing Value Imputation)

·       Building Linear Regression Model

·       Understanding model metrics (p-value, R-square/Adjusted R-square etc.)

·       Multicollinearity (VIF)

·       Model Validation (MAPE, RMSE)

Case study

·       Introduction

·       Assumptions (Linearity, Heteroskedasticity, Multivariate Normality, etc.)

·       Data Preparation (Outlier Treatment, Missing Value Imputation)

·       Building Linear Regression Model

·       Understanding model metrics (p-value, R-square/Adjusted R-square etc.)

·       Multicollinearity (VIF)

·       Model Validation (MAPE, RMSE)

Case study

·       Introduction

·       Linear Regression Vs. Logistic Regression

·       Data Preparation (Outlier Treatment, Missing Value Imputation, Dummy Variable Creation)

·       Building Logistic Regression Model

·       Understanding model metrics (p-value)

·       Multicollinearity (VIF)

·       Model Validation (Confusion Matrix, ROC curve, AUC, etc.)

Case study

·       Introduction

·       Linear Regression Vs. Logistic Regression

·       Data Preparation (Outlier Treatment, Missing Value Imputation, Dummy Variable Creation)

·       Building Logistic Regression Model

·       Understanding model metrics (p-value)

·       Multicollinearity (VIF)

·       Model Validation (Confusion Matrix, ROC curve, AUC, etc.)

Case study

·       Introduction

·       Types

·       Entropy, Gini Index, Chi-Square

·       Overfitting

·       Pruning

·       Cross – Validation

Case study

·       Introduction

·       Types

·       Entropy, Gini Index, Chi-Square

·       Overfitting

·       Pruning

·       Cross – Validation

Case study

·       Introduction

·       Bagging

o   Random forest

·       Boosting

o   Gradient Boosting Machines (GBM)

Case study

·       Introduction

·       Bagging

o   Random forest

·       Boosting

o   Gradient Boosting Machines (GBM)

Case study

·       Introduction

·       Bagging

o   Random forest

·       Boosting

o   Gradient Boosting Machines (GBM)

Case study

·       Introduction

·       Working of KNN

·       Optimal value of K

Case study

·       Introduction

·       K -Means Clustering

·       Cluster Evaluation and Profiling

Case study

·       Introduction

·       Curse of dimensionality

·       Process of working

 

Case study

·       Introduction

·       Curse of dimensionality

·       Process of working

 

Case study

·       Introduction

·       Contamination Factor

Case study

·       Introduction

·       Contamination Factor

Case study

·       Introduction

·       Time Series Components: Trend, Seasonality, Cyclicity

·       Smoothening Techniques– Moving Averages, Exponential

·       ARIMA

·       Accuracy

Case study

·       Introduction

·       Text Pre-processing

o   Noise Removal

o   Lemmatization

o   Stemming

o   Feature Engineering on Text Data

o   Bag of words

o   TF-IDF

Case study

·       Introduction

·       Text Pre-processing

o   Noise Removal

o   Lemmatization

o   Stemming

o   Feature Engineering on Text Data

o   Bag of words

o   TF-IDF

Case study

·       Introduction: Deep Learning

·       Deep Learning vs Machine learning

·       Neural Networks

·       Activation Functions, hidden layers, hidden units

·       Backpropagation

·       Vanishing Gradient Problem

·       Exploding Gradient Problem

·       Perceptron & Multi-layer Perceptron

Case study

·       Introduction: Deep Learning

·       Deep Learning vs Machine learning

·       Neural Networks

·       Activation Functions, hidden layers, hidden units

·       Backpropagation

·       Vanishing Gradient Problem

·       Exploding Gradient Problem

·       Perceptron & Multi-layer Perceptron

Case study

·       Introduction: Deep Learning

·       Deep Learning vs Machine learning

·       Neural Networks

·       Activation Functions, hidden layers, hidden units

·       Backpropagation

·       Vanishing Gradient Problem

·       Exploding Gradient Problem

·       Perceptron & Multi-layer Perceptron

Case study