Why enroll

Unlock the secrets of data-driven decision making with our Data Analytics with Python course! Learn to harness the power of Python's cutting-edge libraries, including Pandas, NumPy, and Scikit-learn, to extract insights, visualize trends, and predict future outcomes. With hands-on projects and expert instruction, you'll become a master data analyst, equipped to drive business success and stay ahead of the curve. Join the data revolution and transform your career - enroll now and start analyzing your way to the top!

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Course content

The course is readily available, allowing learners to start and complete it at their own pace.

Data Analytics With Python

60 Lectures

1665 min

Introduction to Data Analytics
34 min
Python Fundamentals -I
26 min
Python Fundamentals -II
36 min
Central Tendency and Dispersion - I
31 min
Central Tendency and Dispersion - II
32 min
Introduction to Probability-I
28 min
Introduction to Probability-II
29 min
Probability Distribution - I
28 min
Probability Distribution - II
29 min
Probability Distributions - III
26 min
Python Demo for Distribution
21 min
Sampling and Sampling Distribution
34 min
Distribution of Sample Means, population, and variance
24 min
Confidence interval estimation: Single population - I
26 min
Confidence Interval Estimation: Single Population - II
19 min
Hypothesis Testing- I
32 min
Hypothesis Testing- II
26 min
Hypothesis Testing-III
25 min
Errors in Hypothesis Testing
43 min
Hypothesis Testing about the Difference in Two Sample Means
29 min
Hypothesis testing : Two sample test -II
29 min
Hypothesis Testing: Two sample test - III
25 min
ANOVA- I
22 min
ANOVA- II
23 min
Post Hoc Analysis(Tukey’s test)
36 min
Randomize block design (RBD)
26 min
Two Way ANOVA
26 min
Linear Regression - I
35 min
Linear Regression - II
22 min
Linear Regression-III
29 min
Estimation, Prediction of Regression Model Residual Analysis
22 min
Estimation, Prediction of Regression Model Residual Analysis - II
25 min
MULTIPLE REGRESSION MODEL - I
30 min
MULTIPLE REGRESSION MODEL - II
34 min
Categorical variable regression
34 min
Maximum Likelihood Estimation- I
25 min
Maximum Likelihood Estimation- II
29 min
LOGISTIC REGRESSION- I
28 min
LOGISTIC REGRESSION- II
25 min
Linear Regression Model Vs Logistic Regression Model
29 min
Confusion matrix and ROC- I
30 min
Confusion matrix and ROC- II
29 min
Performance of Logistic Model-III
25 min
Regression Analysis Model Building - I
23 min
Regression Analysis Model Building - II
24 min
Chi - Square Test of Independence - I
31 min
Chi - Square Test of Independence - II
28 min
Chi-Square Goodness of Fit Test
25 min
Cluster analysis: Introduction- I
22 min
Cluster analysis: Introduction- II
21 min
Clustering analysis: Part III
27 min
Cluster analysis: Part IV
28 min
Cluster analysis: Part V
19 min
K- Means Clustering
27 min
Hierarchical method of clustering -I
28 min
Hierarchical method of clustering -II
30 min
Classification and Regression Trees (CART : I)
33 min
Measures of attribute selection
27 min
Attribute selection Measures in CART : II
25 min
Classification and Regression Trees (CART) - III
31 min

Course details

Course suitable for

Aerospace
Data Science & Analysis

Key topics covered

1. Introduction to Data Analytics and Python

2. Data Preprocessing and Cleaning

3. Data Visualization and Communication

4. Statistical Analysis and Modeling

5. Machine Learning and Predictive Analytics

6. Working with Big Data and NoSQL Databases

7. Data Storytelling and Presentation

FREE

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