Python for Data Analysis

Description

This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder.

You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.

There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds.

Our Python for Data Analysis is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace.

Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc.

19 sections • 99 lectures • 00:08:00 total length

•Welcome & Course Overview: 00:07:00

•Set-up the Environment for the Course (lecture 1): 00:09:00

•Set-up the Environment for the Course (lecture 2): 00:25:00

•Two other options to setup environment: 00:04:00

•Python data types Part 1: 00:21:00

•Python Data Types Part 2: 00:15:00

•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00

•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00

•Python Essentials Exercises Overview: 00:02:00

•Python Essentials Exercises Solutions: 00:22:00

•What is Numpy? A brief introduction and installation instructions.: 00:03:00

•NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00

•NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00

•NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00

•NumPy Essentials Exercises Overview: 00:02:00

•NumPy Essentials Exercises Solutions: 00:25:00

•What is pandas? A brief introduction and installation instructions.: 00:02:00

•Pandas Introduction: 00:02:00

•Pandas Essentials - Pandas Data Structures - Series: 00:20:00

•Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00

•Pandas Essentials - Handling Missing Data: 00:12:00

•Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00

•Pandas Essentials - Groupby: 00:10:00

•Pandas Essentials - Useful Methods and Operations: 00:26:00

•Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00

•Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00

•Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00

•Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00

•Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00

•Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00

•Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00

•Matplotlib Essentials - Exercises Overview: 00:06:00

•Matplotlib Essentials - Exercises Solutions: 00:21:00

•Seaborn - Introduction & Installation: 00:04:00

•Seaborn - Distribution Plots: 00:25:00

•Seaborn - Categorical Plots (Part 1): 00:21:00

•Seaborn - Categorical Plots (Part 2): 00:16:00

•Seborn-Axis Grids: 00:25:00

•Seaborn - Matrix Plots: 00:13:00

•Seaborn - Regression Plots: 00:11:00

•Seaborn - Controlling Figure Aesthetics: 00:10:00

•Seaborn - Exercises Overview: 00:04:00

•Seaborn - Exercise Solutions: 00:19:00

•Pandas Built-in Data Visualization: 00:34:00

•Pandas Data Visualization Exercises Overview: 00:03:00

•Panda Data Visualization Exercises Solutions: 00:13:00

•Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00

•Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00

•Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00

•Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00

•Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00

•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00

•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00

•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00

•Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00

•Introduction to ML - What, Why and Types..: 00:15:00

•Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00

•scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00

•scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00

•Good to know! How to save and load your trained Machine Learning Model!: 00:01:00

•scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00

•scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00

•Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00

•scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00

•scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00

•scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00

•scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00

•scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00

•Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00

•scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00

•scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00

•scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00

•Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00

•scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00

•scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00

•scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00

•Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00

•scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00

•scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00

•scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00

•scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00

•Theory: K Means Clustering, Elbow method ..: 00:11:00

•scikit-learn - K Means Clustering - Hands-on: 00:23:00

•scikit-learn - K Means Clustering (Project Overview): 00:07:00

•scikit-learn - K Means Clustering (Project Solutions): 00:22:00

•Theory: Principal Component Analysis (PCA): 00:09:00

•scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00

•scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00

•scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00

•Theory: Recommender Systems their Types and Importance: 00:06:00

•Python for Recommender Systems - Hands-on (Part 1): 00:18:00

•Python for Recommender Systems - - Hands-on (Part 2): 00:19:00

•Natural Language Processing (NLP) - (Theory Lecture): 00:13:00

•NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00

•NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00

•NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00

•NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00

•NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00

•Resources- Python for Data Analysis: 00:00:00