Data Science With PYTHON – From Data Analyst To Data Scientist

This journey will first provide a foundation of data architecture, statistics, and data analysis programming skills using Python and R which will be the first step in acquiring the knowledge to transition away from using disparate and legacy data sources. You will then learn to wrangle the data using Python and R and integrate that data with Spark and Hadoop. Next you will learn how to operationalize and scale data while considering compliance and governance. To complete the journey, you will then learn how to take that data and visualize it, to inform smart business decisions.

This learning path, with more than 120 hours of online content, is divided into the following four tracks:

• Data Science Track 1: Data Analyst
• Data Science Track 2: Data Wrangler
• Data Science Track 3: Data Ops
• Data Science Track 4: Data Scientist

Data Science Track 1: Data Analyst

In this track, the focus is the data analyst role with a focus on: Python, R, architecture, statistics, and Spark.

Content:

E-learning courses

• Data Architecture Primer & Data Engineering Fundamentals
• Python for Data Science
• R for Data Science
• Data Science Statistics
• Accessing Data with Spark
• Getting Started with Hadoop
• Hadoop HDFS
• Data Silos, Lakes, & Streams

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 65 minutes

Practice Labs: Analyzing Data with Python (estimated duration: 8 hours)

• Practice performing data analysis tasks using Python by configuring VSCode, loading data from SQLite into Pandas, grouping data and using box plots. Then, test your skills by answering assessment questions after using Python to calculate frequency distribution, measures of center, and coefficient of dispersion. This lab provides access to several tools commonly used in data science, including:
  • VS Code, Anaconda, Jupyter Notebook + Hub, Pandas, NumPy, SiPy, Seaborn Library, Spyder IDE

Data Science Track 2: Data Wrangler

In this track, the focus will be on the data wrangler role. We will explore areas such as: wrangling with Python, Mongo, and Hadoop.

Content:

E-learning courses

• Data Wrangling with Pandas
• Data Wrangler 4
• Data Tools
• MongoDB for Data Wrangling
• Getting Started with Hive
• Getting Started with Hadoop
• Accessing Data with Spark
• Data Lake
• Data Architecture - Deep Dive

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Data Wrangling with Python (estimated duration: 8 hours)

• Perform data wrangling tasks including using a Pandas DataFrame to convert multiple Excel sheets to separate JSON documents, extract a table from an HTML file, use mean substitution and convert dates within a DataFrame. Then, test your skills by answering assessment questions after using a Pandas DataFrame to convert a CSV document to a JSON document, replace missing values with a default value, split a column with a delimiter and combine two columns by concatenating text.

Data Science Track 3: Data Ops

The tracks objective is to help prepare the learner for a Data Ops role with a focus on governance, security, and harnessing volume and velocity.

Content:

E-learning courses

• Delivering Dashboards
• Cloud Data Architecture
• Compliance Issues and Strategies & Implementing Governance Strategies
• Data Access & Governance Policies
• Streaming Data Architectures
• Scalable Data Architectures
• Data Pipeline & Data Sources
• Securing Big Data Streams & Harnessing Data Volume & Velocity
• Data Rollbacks

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Implementing Data Ops with Python (estimated duration: 8 hours)

• Perform data ops tasks with Python including working with row subsets, creating new columns with Regex, performing joins and spreading rows. Then, test your skills by answering assessment questions after working with field subsets and computed columns, and performing set operations and binding rows.

Data Science Track 4: Data Scientist

For this track, the focus will be on the Data Scientist role. Here we will explore areas such as: visualization, APIs, and ML and DL algorithms.

Content:

E-learning courses

• Data Driven Organizations
• Raw Data to Insights
• Storytelling with Data
• Python for Data Science
• R for Data Science
• Advanced Visualizations & Dashboards
• Data Insights, Anomalies, & Verification
• Data Research Techniques
• Machine & Deep Learning Algorithms
• Creating Data APIs Using Node.js

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Data Visualization with Python (estimated duration: 8 hours)

• Perform data visualization tasks with Python such as creating scatter plots, plotting linear regression, using logistic regression and creating decision tree. Then, test your skills by answering assessment questions after creating time-series graphs, resampling observations, creating histograms and using a grid pair.