The Programming Foundations in Python course is an introduction to the basic programming principles of Python. Students will develop a foundational knowledge of programming concepts, algorithms, design patterns, and theory. Language-specific concepts will be covered, outlining the nuances associated with Python. Objectives: Learn about common programming concepts. Learn the basics of iteration, decision making, and code organization. Learn the basics of Object-Oriented Programming. Learn how to handle errors and recover from failure states. Learn how to debug applications.

Programming Foundations - Python

The Introduction to Big Data course introduces students to Big Data on a conceptual level and gives students exposure and practice with several skills and tools currently in use. These skills are taught at a manageable level. Scale-up methods will be used to help students grasp the meaning and popularity of analyzing substantial amounts of data. Students will learn the foundational concepts of Big Data and will know how to move from Big Data basics to more business-specific needs and requirements. Objectives: Explain the background and evolution of Big Data. Learn the fundamentals of MapReduce. Hands on MapReduce experience. Learn how to scale up to large datasets using available tools. Learn how Hadoop and MapReduce utilize multiple computing clusters.

Introduction to Big Data

This course will teach students how to create complex simulations and models for their data to include linear, logistic, stepwise, and exponential regression. Objectives: Use common modeling techniques: Simple linear regression, Non-linear regression, Logistic regression, Step-wise regression. Using simulation to predict future events and reduce risk.

Modeling and Optimization

The Machine Learning course will introduce students to several commonly-used machine learning methods. Students will learn how to determine the best methods for a given set of data and how to use common software tools to utilize these methods. Objectives: Use common machine learning techniques: Decision Trees, Random Forests, Clustering, Supervised and unsupervised learning, Bayesian Networks.

Machine Learning

The Intermediate Statistics course is designed to teach students about hypothesis testing under multiple scenarios. Students will learn how to determine which hypothesis test to utilize and how to perform that test. Students will also learn to identify and verify the data requirements for each hypothesis test. Objectives: Understand Type I and Type II errors. Use one- and two-sample z-test and t-test. Use the ANOVA. Use one and two proportion testing. Use Chi-square test for dependence. Determine which test to utilize.

Intermediate Statistics

The Data Wrangling and Visualization course is designed to help students understand that the heavy lifting in any analysis happens before the analytical procedure starts. Data wrangling is the process of changing the structure and format of raw data until the data are compatible with sometimes rigid requirements for analysis. Data wrangling also includes a quick sanity check of data quality. Data Visualization will give students an understanding and appreciation of the power in representing data graphically. Objectives: Extract important data from an existing data set. Data table manipulations (stacking, splitting, summarizing, and joining) to change table format. Screen data to improve data quality and reduce noise. Decide which data to include and exclude from their analyses. Explain required input formats for various analyses. Understand the impact that metadata has on analyses. Create various methods of visualizing data using common tools: boxplot, histogram, scatterplot, line chart, treemap, heat map, Pareto chart, sparklines, dynamic charts. Attach meaningful statistics and analyses to the appropriate visualization.

Data Wrangling and Visualization

The Metrics and Data Processing course will prepare students to be able to create new metrics that directly answer or monitor business questions. This module will also teach the theory and practice of statistical process control. Upon completion of this module, students will be equipped to help businesses monitor their processes and know when a process is out-of-control and needs to be fixed. Objectives: Explain how to use existing metrics to monitor business processes. Uncover metrics. Create new metrics. Explain the theory and rules of statistical process control (SPC)Explain the difference between control limits and spec limits, and how to fix both types of limit violations. Use capability indices. Use several of the most common types of control charts. Use various analytical tools included with the Pandas package for Python.

Metrics and Data Processing

The Statistical Programming course teaches students how to load R and R Studio onto their PC. Students will then learn basic scripting commands and will be introduced to a vast library of functions to perform various statistical analyses. Objectives: Explain the idiosyncrasies of R compared to other languages. Create a script file to execute complex commands repeatedly. Load a data set from a file. Use R to manipulate data (filtering, coding, sorting, creating new variables). Understand the usage of 'if', 'for', and 'while'. Understand vectors. Use basic visualization tools.

Statistical Programming in R

The Basic Statistics course will help students gain a fundamental understanding of statistical concepts used throughout the Data Science program. Topics covered include probability, data types, common distributions, common descriptive statistics, and statistical inference. Objectives: Use probability rules and Bayes Theorem. Explain the difference between continuous and categorical variables. Use common descriptive statistics for both populations and samples. Use common distributions including Uniform, Normal, z-distribution, t-distribution, F-distribution, Poisson, and Weibul. Use the Central Limit Theorem. Explain what statistical inference is, and how to create confidence intervals. Demonstrate high-level data exploration.

Basic Statistics

*UPGRADE IN PROGRESS* -- CareerCircle platform upgrades will occur between Monday June 27th 10PM Eastern Time through Tuesday June 28th 4AM Eastern Time.&nbsp; The system may be intermittently unavailable during this timeframe.&nbsp;&nbsp;

*UPGRADE IN PROGRESS* -- CareerCircle is upgrading its capabilities and as a result the system may be temporarily unavailable as we bring new capabilities and infrastructure online to better serve our members.&nbsp; If you receive an error during this timeframe please bear with us as we're working to improve our system and you may need to retry that operation at a later time.&nbsp; &nbsp;

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