Analyzing Data With Python
- Created By raju2006
- Last Updated December 11th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Elevate your Python skills and embark on a journey into the world of data analytics with our dynamic class. Dive into the realms of data manipulation and analysis, where you'll explore essential concepts like DataFrames and data visualization techniques. We'll harness the power of Python's data science libraries, including Pandas, Numpy, and Matplotlib, to unlock the potential of your data. In addition, we'll touch on machine learning libraries like Scikit-learn to broaden your knowledge. By the course's conclusion, you'll be proficient in various data analytics tasks, including data preparation, aggregation, summarization, pattern identification, and the automation of manual processes—all achieved through the art of Python coding. Whether you're a novice or looking to enhance your data analytics capabilities, this course is your gateway to mastering Python's role in the world of data analysis and automation
Course Code/Duration:
BDT163 / 3 Days
Learning Objectives:
The course includes presentations, demonstrations, and hands-on labs.
Day 1- (Morning)
- Review Python common functionalities and data structures used in data science.
- Learn the most important Python libraries in data science (Pandas, Numpy, Matplotlib).
- Hands on: Python functionalities and dataframes.
Day 1- (Afternoon)
- Read and write data from/to different formats (excel, csv, text, json, etc.).
- Cleanse and select important records from dataframes.
- Deal with missing data: identify, replace, and eliminate records.
- Sort dataframes by multiple columns.
- Hands on: Data manipulations with Pandas.
Day 2- (Morning)
- Leverage the functions apply, lambda, filter, and map.
- Merge/Join dataframes by foreign keys.
- Learn pivot tables in Pandas.
- Hands on: Data aggregation and summarization.
Day 2-(Afternoon)
- Learn data visualizations with the libraries Matplotlib and Seaborn.
- Introduction to the Machine Learning library Sklearn.
- Apply linear and logistic regression with Sklearn.
- Hands on: Data predictions
Training material provided: Yes (Digital format)
- Basic knowledge of Python, or other programming languages
- Able to write a Python script that gives the character count for the text “Hello world”
- Given two lists (e.g. x=[‘a’, ‘b’, ‘c’] and y=[‘d’, ‘a’, ‘e’]), being able to find:
- the common elements in the two lists
- the elements in x but not in y and vice versa
- Analyst, programmer, business user or anyone interested using data analysis with some basic coding
- Analyst, programmer, business user or anyone interested using data analysis with some basic coding
