
Explore the fundamentals of data science and data manipulation with pandas in Python, covering data transfer, visualization, and building beginner to intermediate machine learning models.
Explore pandas as an open source, high-performance data structure for python, and follow straightforward installation steps with the Anaconda distribution or manual setup to start manipulating data.
Learn to load data from csv, tsv, tab-delimited, and other sources with pandas, then perform quick cleaning and column manipulations to prep data for analysis.
Learn initial data study and preparation by selecting columns, previewing rows with head, filtering data, and using describe, shape, and data types to understand the dataset.
Learn to massage data in pandas by concatenating columns, renaming and selecting fields, and filtering with sorting and basic type conversions in Python.
Learn data analysis in pandas and Python with string manipulations and aggregations. Master filtering, sorting, group by, min and max, and string methods like upper, lower, contains, and replace.
Use loc to subset data by rows and columns, with range and conditional filters, and dropna to remove missing values for clean data analysis.
Visualize data with plots and charts, and merge data frames using joins while pivoting to create pivot tables that summarize across dates.
Master the Pandas shift function to reference previous rows, read and write csvs, and compute daily and weekly returns from a stock dataset.
Are you completely new to Data science?
Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?
Do you want to start or switch career to Data Science and analytics?
If yes, then I have a new course for you. This is module # 2 of the Data Science series. In this course, I cover the absolute basics data analysis and manipulation techniques using Pandas. This course will not cover every syntax available in Pandas, but will take you a level where you can do basic to intermediate data analysis, before proceeding towards feeding it to a data science algorithm.
If are you new to data science, I would recommend you to please take Module # 1(Introduction to Data Science using Python) before starting with this course. We will go through commonly used terms and write plenty of code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started.
I will be using Python and Pandas in a series of such courses. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.
This course will the second data science course in a series of courses. Consider this course as a 101 level data manipulation course, where I cover enough to raise your curiosity in the field of Data Science and Analytics.
The other modules will cover more complex concepts.