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Data Science Certification using Python

Enrolled: 2345 students
Duration: 60 Hrs
Lectures: 88
Level: Intermediate

Introduction to Data Science

1
What is Data Science?
2
Analytics Landscape
3
Life Cycle of a Data Science Projects
4
Data Science Tools & Technologies

Statistical Learning -1 (Including ANOVA)

1
Measures of Central Tendency in Data
2
Measures of Dispersion
3
Understanding Skewness in Data
4
Probability Theory
5
Bayes Theorem
6
Probability Distributions
7
Hypothesis Testing

Statistical Learning - 2 (Including ANOVA)

1
Analysis of Variance and Covariance
2
One way analysis of variance
3
Assumption of ANOVA
4
Statistics associated with one way analysis of variance
5
Interpreting the ANOVA Results
6
Two way analysis of variance
7
Interpreting the ANOVA Results
8
Analysis of Covariance

Data Science with Python

Basics of Python for Data Science

1
Python Basics
2
Data Structures in Python
3
Control & Loop Statements in Python
4
Functions & Classes in Python
5
Working with Data

Data Frame Manipulation with Pandas

1
Data Acquisition(Import & Export)
2
Indexing
3
Selection and Filtering Sorting
4
Descriptive Statistics
5
Combining and Merging Data Frames
6
Removing Duplicates
7
Discretization and Binning
8
String Manipulation

Exploration Data Analysis with Python

1
What is EDA?
2
Processes in EDA
3
Handling Data Types
4
Univariate and Bivariate Analysis
5
Hypothesis Testing

Time Series Forecasting

1
Understand Time Series Data
2
Visualizing TIme Series Components
3
Exponential Smoothing
4
Holt’s Model
5
Holt-Winter’s Model
6
ARIMA

Project : Forecasting and Predicting the furniture sales using ARIMA

Clustering

1
What is Clustering?
2
K-means Algorithm
3
Types of Clustering
4
Evaluating K-means Clusters

Project : Grouping teen students for targeted marketing campaigns

1
Project : Grouping teen students for targeted marketing campaigns

Dimensionality Reduction

1
Principal Component Analysis (PCA)
2
Scatter plot
3
One-eigen value criterion
4
Factor Analysis
5
Project : Reduce Data Dimensionality for a House Attribute Dataset using PCA

Machine Learning & Linear Regression

1
Machine Learning Modelling Flow
2
How to treat Data in ML
3
Parametric & Non-parametric ML
4
Types of Machine Learning
5
Introduction to Linear Regression
6
Linear Regression using Gradient Descent
7
Linear Regression using OLS
8
Linear Regression using Stochastic Gradient Descent
9
Project : Real Estate Price Prediction using Linear Regression

Logistic Regression

1
Introduction to Logistic Regression
2
Logistic Regression using Stochastic Gradient Descent
3
Project 8 Project : Real Estate Price Prediction using Linear Regression

Model Tuning

1
Performance Measures
2
Bias-Variance Trade-Off
3
Overfitting & Underfitting
4
Optimization Techniques
5
Project : Identifying good and bad customers for granting credit

K Nearest Neighbor

1
K Nearest Neighbor
2
Understanding KNN
3
Voronoi Tessellation
4
Choosing K
5
Distance Metrics – Euclideam, Manhattan, Chebyshev
6
Project : Case Study: Breast Cancer

Decision Tree & Random Forest

1
Decision Tree & Random Forest
2
Fundamental concepts of Ensemble
3
Hyper-Parameters
4
Project 11: Case Study : Predicting bank term deposit subscription based on marketing data

Support Vector Machine

1
Support Vector Machines
2
What is SVM?
3
When to use SVM?
4
What is Support Vector?
5
Understanding Hyperplane
6
Project 12: Predicting credibility of the credit card customers

Resume Building and Interview Prep

1
1:1 Mock Interviews with Industry Veterans to Clear the Technical Round of Interviews to Give You Confidence to Face Real World Scenarios

1:1 Mock Interviews

1
1:1 Mock Interviews with Industry Veterans to Clear the Technical Round of Interviews to Give You Confidence to Face Real World Scenarios
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