Data Science for Beginners
You are interested in learning everything necessary to become a Data Scientist and join the ranks of the fastest growing jobs in IT.
So what does a Data Scientist do? The short answer is many things. This is both good and bad because while there are many complex ideas, thoughts, concepts, and skills that you will need to learn and master; there are an equal number of different roles that someone can fill.
Historically, data scientist roles went to statisticians, scientists, or mathematicians who were familiar with tools like SAS, SPSS, or Mathlab. All of these tools are powerful and extremely expensive, which helped drive interest for a cheaper alternative like R or RStudio. These are open source tools that provide the data cleanup, munging, analysis, and visualizations at almost the same level as the expensive enterprise level packages.
The good news is that there is a huge demand for Data Scientists to help with Big Data projects. These are opportunities for individuals that will not have all of the skills traditionally required like advanced mathematics, statistics, programming, or visualization skills. In the end, no one becomes a Data Scientist over night because practitioners must be proficient in many areas. Learning Data Science is a journey of learning many skills, tools, and theories that take time to learn and master. If you are interested in starting your journey to become a master Data Scientist, then this is your first step.
Anova Analytics now offers a Beginning Data Science course, which is 4-Session course that covers instruction and hands on lab sessions.
Data Science Basics $495.00
1) Getting & Loading Your Data
A. Mining Datasets
B. Access Your Data Anywhere
C. Big Data, EDW, CRM, ERP or Social Media
D. Data formats: csv, xlsx, hive, spark, html, API etc.
E. Load Data From local File or remote URL
2) Understand Your Data Using Descriptive Statistics
A. Class Distribution
B. Data Summary
C. Standard Deviations
F. Hands On Lab
3) Understand Your Data Using Data Visualization
A. Get The Best Results with QPlot, GGPlot & GGViz
B. Univariate and Multivariate Visualization
C. Tips For Data Visualization
D. Hands On Lab
4) Prepare Data for Predictive Modeling
A. Data Pre-Processing
B. Scale, Center, Standardize and Normalize Data
C. Box-Cox and Yeo-Johnson Transform
D. Principal Component Analysis Transform
E. Independent Component Analysis Transform
F. Tips For Data Transforms
G. Hands On Lab
If you are interested in learning more about this course, then please complete the following simple form. We will contact you at your convenience. Course payment is due prior to the first day’s course start. This a reoccurring course, so we can accommodate emergency changes in student’s schedules.