Data Science from Scratch

Event Details

Data Science from Scratch

Time: November 25, 2017 from 10am to 5:15pm
Location: Wingify
Street: 402, 4th Floor, Zero One Building, Near Passport office, Mundhwa Road
City/Town: Pune
Website or Map:…
Phone: +91-855-3333-985 / +91-9495-606-678
Event Type: workshop
Organized By: Hari C M
Latest Activity: Nov 20, 2017

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Event Description

Learn fundamental math and take a first dip in the data science ocean in a day-long immersive workshop

For more details and tickets, visit:

We will cover the following in this two part workshop.

Part 1 : Mathematics of data science

A lot of really interested, good programmers and tech professionals seem to maintain distance from data science because they are just plain scared of the math. The workshop will be a refresher of the basic concepts and does not assume any prior knowledge greater than addition, subtraction, multiplication and division.

  1. Indices and logarithms
  2. Functions as transformations.
  3. Combinatorics
  4. Idea of probability as long term frequency of events.
  5. Limits, integration and derivatives.
  6. Application of Derivative for finding minima and maxima. Gradient Descent / Ascent to solving optimization problems.

Part 2 : Tools and Concepts of Data Science

We will cover a high level overview of field of machine learning and introduction to the Python data ecosystem in machine learning. We strongly believe that the best way to learn machine learning is by building few algorithms from scratch. So we will build a supervised ML application from scratch in Python. Since ML is a very vast field, I will spend some time on study guidelines and how to approach the field.

  1. Big picture of machine learning : supervised Vs. unsupervised, generative Vs. discriminative models. Mostly plain English content, covering big picture (~ 20 minutes)
  2. Introduction to Python data ecosystem: few hands on exercises on numpy and pandas to serve as warm-up (~ 30 minutes)
  3. Building a regression/classifier from scratch in Python (~ 45 - 50 minutes), using all the concepts learnt in the first part of the workshop. We complete a full circle about how the concepts from first part of workshop tie up with applications.
  4. Solving a more involved problem by using scikit-learn APIs directly (~ 30 minutes). Introduction to concepts of bias Vs. variance, testing models and feature engineering.
  5. Next steps, how to study and which resources can be used (~ 20 minutes)
  6. Summarizing what we learnt, question-answers (~ 10 minutes.


Harshad Saykhedkar

Head of data science at

Vishal Gokhale

Freelance Programmer

HasGeek has partnered with AWS, and they are offering credits worth $100 to all participants. 

If you have more queries about the event or ticket purchases, contact HasGeek on or  Hari C M (+91-9495606678/+91-8553333985).

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