Python For Data Analysis by MTF Institute

Learn data cleaning, manipulation, statistics, and visualization using Python and the Python Pandas library

What you'll learn

Analyze & interpret data using Python & Pandas

Clean & prepare data (missing values, validation)

Perform statistical analysis using Python

Manipulate & transform datasets using Pandas

Create compelling data visualizations

Requirements

For a better learning experience, we suggest you to use a laptop / mobile phone / pen and paper for taking notes, highlighting important points, and making summaries to reinforce your learning.

No prior data analysis experience is required

Basic familiarity with Python is helpful but not strictly required due to the recap section

A computer with internet access to install Python and necessary libraries

Description

Are you ready to unlock the power hidden within data? In today's world, the ability to analyze data is a crucial skill for success in countless industries. Python has emerged as the undisputed leader in the data analysis landscape, thanks to its versatility, ease of use, and incredibly powerful libraries like Pandas, NumPy, Matplotlib, and Seaborn.This comprehensive course, Python for Data Analysis, is your guide to mastering the essential techniques for working with data using Python. Whether you're an aspiring data analyst, a student, a researcher, or a professional looking to add valuable data skills to your resume, this course provides the practical knowledge you need to turn raw data into actionable insights.You will start by getting comfortable with the Python environment (a quick recap is included if you're a bit rusty) and dive deep into the Pandas library, the cornerstone of data manipulation in Python. Learn how to effectively clean and prepare messy real-world datasets, handling missing values, validating data quality, and transforming data into the right format for analysis.Beyond just cleaning, you'll learn how to perform both descriptive and inferential statistics using Python libraries, including applying regression analysis to understand data patterns and relationships. Discover techniques to merge, join, pivot, and reshape datasets to gain different perspectives on your information. You'll also explore how to handle time series data, a common format in many domains.Communication is key! This course also covers how to create compelling data visualizations using Python's powerful plotting libraries. Learn to represent your findings visually to tell clear and impactful data stories that resonate with stakeholders.Throughout the course, hands-on practice exercises and examples will give you the opportunity to apply what you've learned immediately, building your confidence and practical skills.By the end of this course, you will have the practical skills and confidence to tackle a wide range of data analysis projects using Python, turning raw data into meaningful insights and visualizations. No prior data analysis experience is necessary. Basic Python knowledge is helpful but not strictly required.Enroll today and take the first step towards becoming a skilled data analyst using the power of Python!Course provided by MTF Institute of Management, Technology and FinanceMTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas: Business & Administration, Science & Technology, Banking & Finance. MTF R&D center focused on research activities at areas: Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things. MTF is the official partner of: IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry.MTF is present in 217 countries and has been chosen by more than 775000 students.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Python for Data Analysis

Lecture 2 Course overview

Lecture 3 Python basics recap

Lecture 4 Intro to data analysis

Lecture 5 Intro to Pandas

Lecture 6 File handling data storage

Lecture 7 Descriptive statistics

Lecture 8 Handling missing data

Lecture 9 Data validation and quality control

Lecture 10 Inferential statistics

Lecture 11 Regression analysis

Lecture 12 Merging and joining datasets

Lecture 13 Handling time series data

Lecture 14 Pivot tables and reshaping

Lecture 15 Introduction to visualization libraries

Lecture 16 Expanding your visualization toolkit

Lecture 17 Lets practice

Section 3: Practical exercises

Lecture 18 Let's practice

Lecture 19 Exercise 1

Lecture 20 Exercise 2

Lecture 21 Exercise 3

Lecture 22 Final words

Professionals in any field looking to gain powerful data analysis skills using Python,Aspiring Data Analysts and Business Analysts,Students and researchers who need to analyze data programmatically,Anyone interested in learning practical data science fundamentals with Python