Programming for Data Science
Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems
Of these Data Analysis forms the chunk with a coverage of all Machine Learning algorithms - Regression, Clustering, Market Basket Analysis, Classification and Network Analysis and Recommendation Systems.
A programming language like R or Python and all above data task related packages are taught in such a program.
DATA SCIENCE SYLLABUS - Top Down Methodology
Section 1: Creative code - Computational thinking. Learn how to qualify and express how algorithms work.
Section 2: Building blocks - Breaking it down and building it up
Understand how data can be represented and used as variables and learn to manipulate shape attributes and work with weights and shapes using code.
Section 3: Repetition - Creating and recognising patterns
Explain how and why using repetiton can aid in creating code and begin using repetition to manipulate and visualise data.
Section 4: Choice - Which path to follow
How to create simple and complicated choices and how to create and use decision points in code.
Section 5: Repetition - Going further
Discussing advantages of repetition for data visualisation and applying and reflecting on the power of repetitions in code. Creating curves, shapes and scale data in code.
Section 6: Testing and Debugging
Understanding why and how to comprehensively test your code and debug code examples using line tracing techniques
Section 7: Arranging our data
Exploring how and why arrays are used to represent data and how static and dynamic arrays can be used to represent data.
Section 8: Functions - Reusable code
Understand how functions work in ProcessingJS and demonstate how to deconstruct a problem into useable functions.
Section 9: Data Science in practice
Exploring how data science is used to solve programming problems and how to solve big data problems by applying skills and knowledge learned throughout the course.
Section 10: Where next?
Understand the context of big data in programming and transform a problem description into a complete working solution using the skills and knowledge you've learned throughout the course.
Exploring how you can expand the skills learned in this course by participating in future courses.
Explore the data science process – An Introduction
Section 1: Python Basics
Take your first steps in the world of Python. Discover the different data types and create your first variable.
Section 2: Python Lists
Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.
Section 3: Functions and Packages
Learn how to get the most out of other people's efforts by importing Python packages and calling functions.
Section 4: Numpy
Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.
Section 5: Matplotlib
Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data.
Section 6: Control flow and Pandas
Write conditional constructs to tweak the execution of your scripts and get to know the Pandas
What is Python
Python If else
Python else if
Python nested if
Python for loop
Python while loop
Python do while
Python OOPs Concepts
Python Object Class
Python Files I/O
What is Python?
Python is a high level, object oriented programming language. While Python is very powerful, it’s syntax makes Python simple to learn.
Python is very popular among data scientists, but it is not solely used for analytics. Here is a list of popular applications written in Python:
This website is dedicated to Analytics, so the Python tutorials have been shaped with that in mind. I have purposefully eliminated a lot of information that doesn’t lend itself well to data work. If you are looking for a soup to nuts Python tutorial, this is not it. However, if you are looking for a streamlined education in Python for Analytics, you have found it.
Numpy and Pandas
Supervised Machine Learning
Unsupervised Machine Learning
SQL (Structured Query Language) is one of the most common languages found in the data profession. Don’t let the NoSQL chatter scare you away from learning SQL, as the appearance of applications such as SQL on Hadoop are testimony that no matter what the underlying data structure, SQL is still going to have a strong presence in the data world
Automating Data Preparation with SQL Server
Getting Full Days Worth of Data
MS SQL Server: Database Diagrams
Simple Methods to Recover MDF File Password
Intro to SQL
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Regular batches: Monday – Friday – 2 hrs per day
Weekend Batches: Saturday / Sunday – 5 hrs per day
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