A Guide to Data Science
Created by Lisa Stuart
As soon as the world entered the era of unstructured and big data, the need for its storage and simplification also grew. Data Science became the ultimate solution to this issue. Data Science is an amalgamation of various algorithms, tools, and machine learning principles with the motive to figure out hidden patterns from the raw data. It has a forward-looking approach to analyze the past or current data and predicting the future outcomes with the aim of making data-driven decisions.
Rather than depth, this program focuses on breadth, to give the students a solid foundation from which to integrate and synthesize the concepts learned for application to solving real data problems.
This program will help students master the basic skills necessary to manage and analyze data where they’ll learn about exploratory data analysis, statistical inference and modeling, as well as machine learning principles and techniques. Programming in R, how to wrangle data, what it means to produce reproducible research and how to most effectively communicate results, in addition to other necessary skills for developing data products are among some of the skills that will also be taught in the program.
Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data and thus are looking for a prosperous future in data science. This program will interest students in UG/ PG analytics programs, IT professionals, analytics managers, business analysts, banking and finance professionals, and marketing managers. The career options available after a data science certification range from data scientist to data mining engineer to business intelligence analyst.
- Explain what Data Science is as well as the skill sets necessary to perform data science.
- Perform basic statistical modeling and analysis using R/RStudio.
- Describe the meaning of Statistical Inference and identify common probability distributions used as the foundations for statistical modeling. Fit a model to data and assess the model fit.
- Apply the necessary tools and techniques (summary statistics, graphs, plots) of exploratory data analysis (EDA) and explain the significance of EDA in data science.
- Explain the processes that occur in Data Science and their iterative and interactive points.
- Collect data using APIs and other tools to scrap the Web.
- Use a case study to apply the Data Science Process and perform EDA.
- Apply basic machine learning algorithms for predictive modeling.
- Identify and describe basic Feature Selection algorithms and common approaches used for Feature Engineering.
- For a given data set, identify best visualization type(s).
|Introduction to program, R, RStudio, RMarkdown, GitHub|
|R and Basic Data Exploration|
|Data Wrangling and Exploratory Data Analysis|
|Data Visualization 1: Introduction to ggplot2|
|Data Wrangling with dplyr and tidy|
|Probability and Monte Carlo Simulations|
|Statistical Inference, p-values, Statistical Models|
|Bayesian Statistics, Regression|
|Intro to Machine Learning|
|Decision Trees and Random Forest, Program Wrap-Up|
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