Data Science and Analytics: Ultimate Masterclass
This is one of the most comprehensive courses on Data Science that you can find on the web. The complete Data Science course uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practical exercises on real data for each topic you cover because the goal is "Learn by Doing"!
$23.15
$32.41
1-Year Access
Course Includes:
- 25 Hours Video Class
- Downloadable Resources
- Free Certificate of Completion
- 1 Year Access
Data Science and Analytics: Ultimate Masterclass
260 students enrolled 
108 lectures
Self-paced onlineThis is one of the most comprehensive courses on Data Science that you can find on the web. The complete Data Science course uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practical exercises on real data for each topic you cover because the goal is "Learn by Doing"!
What you will learn
- Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making.
- Python for Data Science and Machine Learning
- NumPy for Numerical Data
- Pandas for Data Analysis
- Plotting with Matplotlib
- Statistical Plots with Seaborn
- Interactive dynamic visualizations of data using Plotly
- SciKit-Learn for Machine Learning
- K-Mean Clustering, Logistic Regression, Linear Regression
- Random Forest and Decision Trees
- Principal Component Analysis (PCA)
- Support Vector Machines
- Natural Language Processing and Spam Filters and more.
Requirements
This course is for you, if you want to learn Data Science with Python, want to learn Machine Learning with Python. The course starts from a beginner's guide to advanced expert, therefore, anyone interested in the topic can begin.
Course Description
This is one of the most comprehensive courses on Data Science that you can find on the web. The complete Data Science course uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly,...
$23.15
$32.41
1-Year Access
Course Includes:
- 25 Hours Video Class
- Downloadable Resources
- Free Certificate of Completion
- 1 Year Access
Course Content
- Course IntroPreview7 Mins
- Set-up the Environment for the Course (lecture 1)Preview10 Mins
- Set-up the Environment for the Course (lecture 2)26 Mins
- Download environment file and watch next lecture to setup -- super easy way1 Mins
- Two other options to setup environment4 Mins
- Python data types Part 121 Mins
- Python Data Types Part 215 Mins
- Comparisons Operators, if, else, elif statement13 Mins
- Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)Preview16 Mins
- Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)21 Mins
- Python Essentials Exercises Overview3 Mins
- Python Essentials Exercises Solutions21 Mins
- What is Numpy? A brief introduction and installation instructions.4 Mins
- Numpy Essentials - Numpy arrays, built-in methods, array methods and attributes28 Mins
- NumPy Essentials - Indexing, slicing, broadcasting & boolean masking27 Mins
- NumPy Essentials - Arithmetic Operations & Universal Functions8 Mins
- NumPy Essentials Exercises Overview3 Mins
- NumPy Essentials Exercises Solutions26 Mins
- What is pandas? A brief introduction and installation instructions.2 Mins
- Pandas Introduction3 Mins
- Pandas Essentials - Pandas Data Structures - Series21 Mins
- Pandas Essentials - Pandas Data Structures - DataFrame30 Mins
- Pandas Essentials - Hierarchical Indexing15 Mins
- Pandas Essentials - Handling Missing Data12 Mins
- Pandas Essentials - Data Wrangling - Combining, merging, joining21 Mins
- Pandas Essentials - Groupby11 Mins
- Pandas Essentials - Useful Methods and Operations27 Mins
- Pandas Essentials - Project 1 (Overview) Customer Purchases Data9 Mins
- Pandas Essentials - Project 1 (Solutions) Customer Purchases Data31 Mins
- Pandas Essentials - Project 2 (Overview) Chicago Payroll Data5 Mins
- Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data18 Mins
- Pandas Essentials - Project 2 (Solutions Part 2) Chicago Payroll Data19 Mins
- Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach14 Mins
- Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach23 Mins
- Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach22 Mins
- Matplotlib Essentials - Exercises Overview6 Mins
- Matplotlib Essentials - Exercises Solutions21 Mins
- Matplotlib Essentials (Optional) - Advance1 Mins
- Seaborn - Introduction & InstallationPreview4 Mins
- Seaborn - Distribution Plots26 Mins
- Seaborn - Categorical Plots (Part 1)21 Mins
- Seaborn - Categorical Plots (Part 2)16 Mins
- Seaborn - Axis Grids26 Mins
- Seaborn - Matrix Plots14 Mins
- Seaborn - Regression Plots12 Mins
- Seaborn - Controlling Figure Aesthetics11 Mins
- Seaborn - Exercises Overview5 Mins
- Seaborn - Exercise Solutions19 Mins
- Pandas Built-in Data Visualization34 Mins
- Pandas Data Visualization Exercises Overview4 Mins
- Panda Data Visualization Exercises Solutions14 Mins
- Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1)20 Mins
- Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2)14 Mins
- Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview)11 Mins
- Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions)37 Mins
- Project 1 - Oil vs Banks Stock Price during recession (Overview)15 Mins
- Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1)18 Mins
- Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2)19 Mins
- Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3)17 Mins
- Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview)3 Mins
- Introduction to ML - What, Why and Types.15 Mins
- Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff15 Mins
- A note on student’s concerns and questions on FutureWarnings.1 Mins
- Scikit-learn - Linear Regression Model - Hands-on (Part 1)18 Mins
- Scikit-learn - Linear Regression Model Hands-on (Part 2)20 Mins
- Good to know! How to save and load your trained Machine Learning Model!2 Mins
- Scikit-learn - Linear Regression Model (Insurance Data Project Overview)9 Mins
- Scikit-learn - Linear Regression Model (Insurance Data Project Solutions)20 Mins
- Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity...etc.11 Mins
- Output of classification report in scikit-learn — A small change1 Mins
- Scikit-learn - Logistic Regression Model - Hands-on (Part 1)17 Mins
- Scikit-learn - Logistic Regression Model - Hands-on (Part 2)20 Mins
- Scikit-learn - Logistic Regression Model - Hands-on (Part 3)12 Mins
- Scikit-learn - Logistic Regression Model - Hands-on (Project Overview)5 Mins
- Scikit-learn - Logistic Regression Model - Hands-on (Project Solutions)15 Mins
- Theory: K Nearest Neighbors, Curse of dimensionality9 Mins
- Scikit-learn - K Nearest Neighbors - Hands-on25 Mins
- Scikt-learn - K Nearest Neighbors (Project Overview)5 Mins
- Scikit-learn - K Nearest Neighbors (Project Solutions)14 Mins
- Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.18 Mins
- Scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1)19 Mins
- Scikit-learn - Decision Tree and Random Forests (Project Overview)5 Mins
- Scikit-learn - Decision Tree and Random Forests (Project Solutions)16 Mins
- Support Vector Machines (SVMs) - (Theory Lecture)7 Mins
- scikit-learn - Support Vector Machines - Hands-on (SVMs)31 Mins
- scikit-learn - Support Vector Machines (Project 1 Overview)7 Mins
- scikit-learn - Support Vector Machines (Project 1 Solutions)21 Mins
- scikit-learn - Support Vector Machines (Optional Project 2 - Overview)2 Mins
- Theory: K Means Clustering, Elbow method12 Mins
- scikit-learn - K Means Clustering - Hands-on24 Mins
- scikit-learn - K Means Clustering (Project Overview)8 Mins
- scikit-learn - K Means Clustering (Project Solutions)22 Mins
- Theory: Principal Component Analysis (PCA)10 Mins
- scikit-learn - Principal Component Analysis (PCA) - Hands-on22 Mins
- scikit-learn - Principal Component Analysis (PCA) - (Project Overview)2 Mins
- scikit-learn - Principal Component Analysis (PCA) - (Project Solutions)22 Mins
- Theory: Recommender Systems their Types and Importance6 Mins
- Python for Recommender Systems - Hands-on (Part 1)18 Mins
- Python for Recommender Systems - - Hands-on (Part 2)19 Mins
- Natural Language Processing (NLP) - (Theory Lecture)13 Mins
- NLTK - NLP-Challenges, Data Sources, Data Processing14 Mins
- NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing19 Mins
- NLTK - NLP - Tokenisation, Text Normalisation, Vectorisation, BoW...19 Mins
- NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes ...14 Mins
- NLTK - NLP - Pipeline feature to assemble several steps for cross-validation...10 Mins
- Please read, it's important!1 Mins
- Thanks you for doing the course!1 Mins
- Downloadable ResourcesMins
About the Instructor
4.5 Stars Rating
258 Students
1 CoursesDr. Qazi has a BS with major in Maths, Statistics & Physics, MS in Computer Science and PhD degree. As a mentor and a researcher scientist, with over 18 years of professional experience, Dr. Qazi has developed a skill set in data cleaning/mining, data analysis & data modelling, project management, teaching & training and career advising while working with academic...
Rating & Reviews
3.3%
JK
Jaiyeola Kajero
Simplify data science and analytics
LM
Laurence Mbikpo
[ls help me with the download especially the exercises....
HY
Hope Yari
please how can download the course material
JOS
Jesse Oliseh Samuel
It very interesting and educative. I enjoy every bit of it. Scikitlearn use to be difficult to understand but, you made it very simple.
Thanks
ABA
Ahmad Bashir Abubakar
It's quite educating. I really learnt a lot from it. Shukriyah he!
JA
Jeremiah Agada
It was really an interesting course. I really wish to have them download.
LA
Luther Atsever
Quite interesting. Looking forward to start the class.
OO
olowookere oluwatobi
$23.15
$32.41