DebKR

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Programming

Programming in Python: First Steps

Programming in Python: First Steps

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python0PTP 1. Programming Stream / B. Python (beginner)

PTP Outcomes:
Familiarise myself with Python language and ecosystem
Be able to do simple coding exercises
Gain an insight into Python’s use in data science, data manipulation and visualisation
Familiarise myself with relevant data analysis packages/libraries

Initial thoughts
As mentioned in my Introduction to Data Science post, I’ve researched and set myself a Personalised Training Plan based on the knowledge/skills I’ll need to learn and develop if I’m to pivot my career away from finance/accountancy and towards data/analytics. As first steps in the Programming and Data Science streams, I’ve signed up for two specialisations at Coursera.org (links here and here). [Read more…] about Programming in Python: First Steps

Introduction to Data Science

Introduction to Data Science

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launchPTP 5. Data Science Stream / A. Introduction to data science

PTP Outcomes:
Learn about the history of data science, where it’s come from, where it’s going to
Get an overview of the main principles, methods and applications of data science
Understand how data scientists develop insights from raw data, and how they present those insights to others
Become familiar with the most important tools, languages, platforms being used by data scientists
Learn about the fundamentals of data engineering

Some thoughts before I being
My Personalised Training Plan has been set after reasonably extensive research online, taking account of topics covered by various online and offline schools/courses, the advice of Analytics Vidhya and Ajit Jaokar on how to move into a data science career, as well as a review of key skills required by data scientists based on current jobs advertised online (e.g. on LinkedIn). [Read more…] about Introduction to Data Science

Diving Into Data: Personalised Training Plan (work in progress)

Diving Into Data: Personalised Training Plan (work in progress)

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training-planBelow is my personalised training plan to move into a career in Data/Analytics with Machine Learning, with specific reference to consumer-direct/e-commerce business. To be implemented throughout March- November 2016.

Areas to cover

Mathematics: Linear algebra, Multivariate calculus

Probability and Statistics: Inferential stats, Descriptive stats, Regression analysis (linear, logistic), Cluster analysis, Classification and regression tree model (CART), Hypothesis testing, Decision trees, Predictive modelling/forecasting [Read more…] about Diving Into Data: Personalised Training Plan (work in progress)

Where to Start in Programming 2

Where to Start in Programming 2

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programming-pythonOption 1: the intention is to create something specific right now (start with the end in mind)
  • Web developing: HTML, CSS, JavaScript, PHP
  • App developing: Java for Android, or Objective C for iOS
  • Mainstream programming, various uses: C, C#, Java, Perl, Python, Ruby

Option 2: the intention is to become a programmer, we can worry about what to create later (eat, sleep, code)

  • To learn to code efficiently, at machine-level, choose C
    • Advantages: you learn the fundamentals of coding; see how the language interacts with the hardware; requires discipline so teaches you to code efficiently
    • Disadvantage: strict; not really beginner-friendly; longer learning time than some other languages; can be frustrating (e.g. debugging for errant semicolons)
  • To learn the principles of Object Oriented Programming (OOP), choose Java
    • Advantages: highly practical language; learning Java means you’re learning the OOP principles – allows for easier/faster learning of more modern languages like C++, Perl, Python, PHP; taught for free via Stanford Uni’s open-access Programming Methodology course; used extensively across many platforms; used in Android app development; long history of use means lots of standard examples available
    • Disadvantages: ??

[Read more…] about Where to Start in Programming 2

Where to Start in Programming

Where to Start in Programming

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programmingIn school we were taught Basic. As a maths undergrad I studied programming, primarily C++. So long ago now, I don’t remember much at all.

If I were to learn programming now, it’s a minefield: What language to concentrate on? What’s the best route for a beginner to follow? What hardware requirements are there so I can get going (economically)? Are there different routes one could follow that determine what kind of programming they could end up doing? Or is there a generic language I can start with, then branch out from there? How long will it take me to learn (to reasonable level of competency) any given programming language?

Some general thoughts and research (in no particular order):

  • Finance systems implementation (current project): Unit4 (formerly CODA) Dream accounting software is “built on Windows technology”
  • Windows OS mainly uses C, C++ with some C# for Windows, also some parts in assembly language
  • XML (Extensible Markup Language): A metalanguage, powerful option for importing/exporting data between different users/applications. Through a set of pre-defined rules, documents or data can be formatted which is machine-readable but also remains readable by humans. Referred to in the book Digital Accounting: The Effects of the Internet and ERP on Accounting by Ashutosh Deshmukh. Used by various governments in ‘tagged’ accounting submissions. Not widely deployed by small businesses despite it’s relative ease to implement. Could be used for (dare I say it) passing information between departments

[Read more…] about Where to Start in Programming