Python for Data Analysis: Unleash the Power of Python for Data Analysis
Dive into the World of Python for Data Analysis
Are you ready to harness the incredible potential of Python for data analysis using Python? Our ‘Data Analytics With Python’ course is your gateway to unlocking the secrets hidden within data through Python for data analysis. Whether you’re an aspiring data scientist, a business analyst, or simply curious about the world of data and Python for data analysis, this course is tailored to equip you with the skills you need.
What You'll Learn with Python for Data Analysis
In this 2-day, instructor-led course, you’ll embark on a journey through the fascinating realm of Python for data analysis. We start with a Python programming refresher, ensuring you have the foundation you need for Python for data analysis. From there, we dive into importing libraries, creating data sets and data frames in Python for data analysis, and mastering file operations for CSV, TXT, and MS Excel, all with Python for data analysis. You’ll even explore database operations with MySQL in Python for data analysis.
The heart of this course lies in introducing you to the powerful Python pandas data science library for Python for data analysis. You’ll learn data manipulation and cleaning techniques, getting comfortable with Series and Data Frame as essential data structures for Python for data analysis. We’ll teach you how to use functions like group by, merge, and pivot tables to analyze data effectively in Python for data analysis. By the end of the course, you’ll be equipped to take raw tabular data, clean it, manipulate it, and perform basic inferential statistical analyses in Python for data analysis. Visualizing your insights will be a breeze too in Python for data analysis!
Your Data Odyssey Begins with Python for Data Analysis
Data is everywhere, and the ability to analyze it is a skill in high demand across industries, especially when leveraging Python for data analysis. Join us on this exciting journey of ‘Data Analytics With Python’ and Python for data analysis. Whether you’re looking to boost your career or satisfy your curiosity about Python for data analysis, we’re here to guide you through the world of Python for data analysis. Get ready to turn data into insights and make informed decisions using Python for data analysis. Your data odyssey begins here with Python for data analysis!
Course Details
Course Duration: 2 days; Instructor-led
This two-day course is designed to provide a comprehensive understanding of data analytics,
pre-processing, and machine learning. On the first day, participants will learn the basics of data analytics, including visualization techniques, data cleaning, and string manipulation. The course will cover the use of “If-Else” statements, row filtering, and handling missing data. An introduction to the CRISP-DM methodology will set the stage for hands-on sessions with both supervised and unsupervised modelling methods.
On the second day, the focus shifts to advanced data transformation techniques, including merging and transforming tables, and column operations such as filtering and splitting. Participants will learn about data collection, pre-processing, and visualization. The course will delve into machine learning, covering the creation and evaluation of predictive models. Various regression and classification models, including decision trees and random forests, will be applied and interpreted in practical scenarios. By the end of the course, participants will have developed the skills necessary to handle real-world data problems and create effective machine learning models.
Audience
Suitable for Line Leaders, Line Managers, Entry Level Recruits and Enthusiasts.
Prerequisites
No prerequisites
Methodology
- Interactive slides
- Hands-on-Experiments
- Group activities
- Assessments and short quiz to evaluate the candidates
Course Objectives
At the end of the module, the candidates will be able to:
- Understand the fundamentals of data analytics and the importance of data cleaning and manipulation.
- Gain proficiency in visualizing, processing, and transforming data.
- Learn and apply both supervised and unsupervised machine learning models.
- Develop skills in creating, evaluating, and interpreting machine learning models to solve real-world data problems.
Outlines
Day 1
- Introduction To Data Exploration
- Visualization: Graphing & Plotting In KNIME
- Data Cleaning
- String To Date: Changing Strings To Dates
- String Manipulation
- Metanodes: Combine Nodes To Clean Up the Process Workflow
- String Manipulation and its Significance
- Use “If-Else” Statements
- Row Filter
- Handling Missing Data
- Modelling Overview With CRISP-DM Data Problem Solving Methodology
- Hands-On With Supervised Modelling Methods
- Hands-On With Unsupervised Modelling Methods
Day 2
- Table Transformation (Merging Data, Table Information, Transpose, Etc.)
- Row Operations (i.e., Filter)
- Column Operations (Filtering, Spiting, Adding, Date Information, Missing
- Values, etc.)
- Data Collection With Reading Nodes
- Pre-Processing And Transforming Data
- Visualizing Data Visual Nodes
- Understanding What Machine Learning Is And Why It Is Important
- Creating Machine Learning Predictive Models And Evaluating Them:
- Simple And Multiple Linear Regression
- Decision Tree Classification
- Decision Tree Regression
- Random Forest Classification
