Over the past decade, the field of data science has grown exponentially — both in the breadth of applications it encompasses, as well as in the depth at which aspiring data scientists are expected to understand its core concepts. In this course, we’ve selected the important lessons that provide a useful roadmap to break into data science, as well as inspiring anecdotes that describe how successful data scientists first entered the field and built their careers.
LEARNING AT SWABHAV
A Curriculum catering to your Goals
Working as a software developer takes alot more than the knowledge of how to program and build a web application.
At Swabhav, students are guided to learn to think and build as software developers — from developing programming mastery to gaining an understanding of how products are designed and managed under the Agile SDLC.
In each three-week curriculum module, students develop key skills through interactive lectures, and close collaboration, showcasing progress through Portfolio Projects.
Module 1: Machine Learning Basics
Lets get going: Syntax Basics!
Machine-learning algorithms find and apply patterns in data. And they pretty much run the world. But first, understand the difference between all the words that are hot in the market: Data Science, Machine Learning, Artificial Intelligence and Deep Learning, Understand the role of a data scientist and get set ready with the installation.
Introduction to Malchine Learning Anaconda Installation
Module 2: Stats, NumPy and Pandas
NumPy is a fundamental Python package to efficiently practice data science. Learn to work with powerful tools in the NumPy array, and get started with data exploration. Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
Module 3:Matplotlib, DataScience and Machine Learning Workflow
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease. Integrate spatial data into your Python Data Science workflow and design machine learing workflows in python.
Module 4: Machinelearning flow, Regression, Classification and Unsupervised Machine Learning
Learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions. Learn to train and assess models performing common machine learning tasks such as classification and clustering. Lastly,be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Machine Learning Workflow Working with Classificaiton Case Study
Working with Regression case study
Unsupervised Machine Learning
Graduate Admission Analysis
400 applicants have been surveyed as potential students for a university. The university weighs certain aspects of a student's education to determine their acceptance. The objective is to explore what kind of data is provided, determine the most important factors that contribute to a student's chance of admission, and select the most accurate model to predict the probability of admission.
Loan Approval Prediction
Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.
Car Price Prediction
You are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.
Careers in Data Science
t’s already been almost nine long years since the famous declaration by the Harvard Business Review on Data Scientist being the “Sexiest Job of the 21st Century”. Since then, the data science field as a whole has matured in rapid ways. Notable among these developments is in the careers, from the rise of data science bootcamps to undergraduate programs in data science.
Data science is one of the hottest fields in Information technology in present scenario. A career in data science requires a thorough understanding of mathematics and statistics. At the heart of data science is machine learning, analytics and statistical skills to draw meaningful insights. Some of the most commonly asked for skills in data scientist are — R, Python, Hadoop, Apache Spark, Scala, machine learning among others. Data scientists are also skilled in knowledge of different data mining techniques such as regression, clustering, decision trees and support vector machines. At its heart, data science is the art of analyzing petabytes and terabytes of data in a short span of time and extracting useful information from huge volumes of data. Over the years, data scientists have successfully created new fields of knowledge such as predictive analytics which is used extensively in manufacturing, retail and healthcare and helps in streamlining operations and bringing down costs significantly.
Companies using Data Science
ADMISSION & PROGRAM DETAILS
Make the Jump
When we say we build a community, we genuinely do. We dont just select an individual student but rather cultivate a group of diverse and unique people with passion for technology.
1. Register yourself HERE
- 2. Complete Technical Application and pass a Code Assessment.
- 3. Deposit your fee and get going with our ONLINE CLASS!!
Online pre-recorded classes with 24*7 guidance by mentors with subject expertise
Project based (Pre-Req: Personal Laptop to work on)