This course will help you learn the fundamentals. It is aimed at complete beginners. You can expect to learn few tips to work quickly and efficiently with technologies like HTML, CSS, and Python. This is course is divided into three parts for your convenience. Finish all the three parts to learn the fundamentals of all the developing technologies.
This course has two Highlights.
- The course will help you learn the techniques used by industry experts and working professionals.
- The course is delivered in simple English language. Most of the complicated terminology is avoided to help you understand easily.
At the end of the course, you are expected to finish a final project.
Did you complete the first part of this course - "Data Science and Machine Learning with Python"
Pre – Requisites
- A desktop computer (Windows, Mac, or Linux) which supports Enthought Canopy 1.6.2 or newer. You will learn the installation process during this course.
- Coding or scripting experience is required.
- Minimum of high school level math skills will be required.
- Develop using Python notebooks
- Understand statistical measures such as standard deviation
- Visualize data distributions, probability mass functions, and probability density functions
- Visualize data with matplotlib
- Use covariance and correlation metrics
- Apply conditional probability for finding correlated features
- Use Bayes' Theorem to identify false positives
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Understand complex multi-level models
- Use train/test and K-Fold cross-validation to choose the right model
- Build a spam classifier using Naive Bayes
- Use decision trees to predict hiring decisions
- Cluster data using K-Means clustering and Support Vector Machines (SVM)
- Build a movie recommender system using item-based and user-based collaborative filtering
- Predict classifications using K-Nearest-Neighbor (KNN)
- Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
- Understand reinforcement learning - and how to build a Pac-Man bot
- Clean your input data to remove outliers
- Implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark's MLLib
- Design and evaluate A/B tests using T-Tests and P-Values
According to Glassdoor, in 2016 data science was the highest paid field to get into. Of course, this follows the basic laws of economics - supply and demand. The demand for data science is very high, while the supply is too low.
What are some examples of data science?
- Google. They are the definition of data science. Everything they do is data driven from their search engine (google.com), through their YouTube efforts, maximization of ad revenue, etc.
- Amazon. Each product recommendation that you get comes from Amazon’s sophisticated data science algorithms.
- Facebook. Facebook is generating ad revenue like crazy since it has all that personal data for all its users. Since you interact with the platform, they know if you prefer cat videos or dog videos, so they know if you are a cat person or a dog person.
“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.”
On any given day, a data scientist may be required to:
- Conduct undirected research and frame open-ended industry questions
- Extract huge volumes of data from multiple internal and external sources
- Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
- Thoroughly clean and prune data to discard irrelevant information
- Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
- Devise data-driven solutions to the most pressing challenges
- Invent new algorithms to solve problems and build new tools to automate work
- Communicate predictions and findings to management and IT departments through effective data visualizations and reports
- Recommend cost-effective changes to existing procedures and strategies
Every company will have a different take on job tasks. Some treat their data scientists as glorified Data Analysts or combine their duties with Data Engineers; others need top-level analytics experts skilled in intense machine learning and data visualizations.