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What you'll learn?
- Learn Python Coding for Exploratory Data Analysis from zero base
- Extensive Examples of Pandas Library for Data Analysis
- Extensive Examples of Numpy Library to handle Multidimensional Arrays
- Complete Data Analysis Project Walkthrough
Course Overview
Are you aspiring to learn Data Science, Machine Learning, or Data Analysis using Python? if yes, then this course on Python will give you the right base, and that too in less than 10 hours.
In this course, you will learn about the basics of the Python Language, Language Elements, Multidimensional Array Handling using the Numpy Library, handling business data using Pandas Library, etc.
You will also learn the tools and techniques of Data Analysis followed by a Data Analysis Project.
Course Sections:
Python Language in Detail
Python internal Data Structures
Python Language Elements
Pandas Data Structure – Series and DataFrames
Exploratory Data Analysis (EDA)
Some of the areas you will master using the powerful Numpy and Pandas Libraries in this course:
Data Structures: Numpy provides arrays that are optimized for numerical operations, while Pandas provides two main data structures - Series and DataFrame. You will learn how to create, manipulate, and use these structures for data analysis.
Data Cleaning: Pandas provides a range of functions to clean and preprocess data. You can learn how to handle missing data, remove duplicates, and deal with data outliers.
Data Aggregation: Pandas provides functions to group data by one or more variables and perform various aggregation operations on the data such as sum, count, mean, and standard deviation. Numpy provides functions to perform mathematical operations on arrays such as sum, mean, max, min, etc.
Data Transformation: Pandas provides functions for transforming data, including reshaping, merging, and pivoting data. Numpy provides functions for slicing and indexing arrays, and for reshaping and manipulating arrays.
Happy Learning!
Pre-requisites
- No Programming background required.
- This course teaches Python in a lucid and well-explanatory manner.
Target Audience
- Beginner Python Developers curious about Data Analysis, Data Science, Machine Learning
Curriculum 37 Lectures 08:53:00
-
Section 1 : Introduction
- Lecture 2 :
- Starting with Python with Jupyter Notebook
- Lecture 3 :
- Python Variables and Conditions
- Lecture 4 :
- Python Iterations 1
- Lecture 5 :
- Python Iterations 2
- Lecture 6 :
- Python Lists
- Lecture 7 :
- Python Tuples
- Lecture 8 :
- Python Dictionaries 1
- Lecture 9 :
- Python Dictionaries 2
- Lecture 10 :
- Python Sets 2
- Lecture 11 :
- Numpy Arrays 1
- Lecture 12 :
- Numpy Arrays 2
- Lecture 13 :
- Numpy Arrays 3
- Lecture 14 :
- Pandas Series 1
- Lecture 15 :
- Pandas Series 2
- Lecture 16 :
- Pandas Series 3
- Lecture 17 :
- Pandas Series 4
- Lecture 18 :
- Pandas DataFrame 1
- Lecture 19 :
- Pandas DataFrame 2
- Lecture 20 :
- Pandas DataFrame 3
- Lecture 21 :
- Pandas DataFrame 4
- Lecture 22 :
- Pandas DataFrame 6
- Lecture 23 :
- Pandas DataFrame 6
- Lecture 24 :
- Python User Defined Functions
- Lecture 25 :
- Python Lambda Functions
- Lecture 26 :
- Python Lambda Functions and Date-Time Operations
- Lecture 27 :
- Python String Operations
- Lecture 28 :
- Python Sets 1
-
Section 2 : Exploratory Data Analysis with Project
- Lecture 1 :
- Introduction
- Lecture 2 :
- Tools and Processes of EDA
- Lecture 3 :
- EDA-Project-1
- Lecture 4 :
- EDA-Project-2
- Lecture 5 :
- EDA-Project-3
- Lecture 6 :
- EDA-Project-4
- Lecture 7 :
- EDA-Project-5
- Lecture 8 :
- EDA-Project-6
- Lecture 9 :
- EDA-Project-7
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Instructor
1811 Course Views
3 Courses
I hold a Master's Degree (MSc) from Liverpool John Moores University (LJMU), UK on Artificial Intelligence and Machine Learning (AI/ML).
My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, areas such as Supervised Learning on Semantic Similarity and so on.
My expertise area also encompass an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised and Clustering methods.
I have > 20 Years of experience in the IT Industry, mostly with the Financial Services domain. Starting as a Developer to being an Architect for a number of Years to Leadership position. The key focus and passion is to increase technical breadth and innovation.
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