## GET THE COMPLETE PATH TO BECOME A DATA SCIENTIST

Countries and counting

A guided series of courses and industry projects to help you navigate through all the steps to crack the data science role. Explained by someone who has done it before.

- Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
- Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
- Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.

4.7/5 (1024 ratings)

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## 220,000 +

Professionals Trained

## 250 +

Workshops every monthas

## 70 +

## What you will learn

### 01

## Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

### 02

## Python Distribution

### 03

## Python Distribution

### 04

## Python Distribution

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## Python Distribution

### 06

## Python Distribution

## Course Curriculum

11 Sections • 33 Lectures • 4h 51 min total length

### Learning Objective of the Course

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## Requirements

- Basics of Python
- Foundational knowledge of Data Science
- High school maths

## Who should attend this course?

- Professionals in the field of data science

- Professionals looking for a robust, structured Python learning program

- Software or data engineers interested in quantitative analysis

- Professionals working with large datasets

- Data analysts, economists, researchers

## About the course

Malware attacks affect not just individual consumers, but also enterprises and governments. And as a provider of operating system software, Microsoft takes this problem very seriously.

In this course you will solve this problem by predicting whether a computer is going to be attacked by malware or not. You’ll learn end-to-end project steps, in-depth concepts, real world tips and tricks, and the full code involved in building the actual data science solution.

You will learn the following skills by the end of the course:

LightGBM
XGBoost Random
Forest Decision Tree
Logistic Regression
Hyperparameter
Tuning Feature Importance Confusion Matrix
ROC AUC
Concordance and Discordance
Precision Recall Curve
Capture Rates and Gains
Feature Engineering
Label Encoding
Frequency Encoding
Chi-Square test ANOVA test
Exploratory Data Analysis
Memory
Optimization
Data Preprocessing

## Instructor

## Selva Prabhakaran

#### Principal Data Scientist

###
4.5

Instructor rating

###
2,343

reviews

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102,432

students

###
9

Courses

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## FAQs

This is a completely self-paced online project course – you decide when you start and when you finish. On an average, students have finished this project course in 2-3 weeks.