Python for Data Analysis Professional Concentration

For several years, Python has been globally ranked as the top programming language used in industry, and is a logical stepping stone to learning more complicated programming languages like C++ and Java. As the first course in our Python for Data Analysis Professional Concentration, this online course is designed for professionals looking to develop relevant software skills and is suitable for anyone looking for a structured and focused introduction to programming, especially for professionals in non-technical roles looking to switch to a more technical career. 

Through remote lectures, group discussions and hands-on activities, this beginner course will give you a foundational understanding of programming and open doors for professional development. By the end of this course, you will have enough technical knowledge and Python programming experience to further explore on your own or in more advanced courses. 

Learning Outcomes

• Write and execute Python code in the Jupyter Notebook environment 
• Use Python data structures and control flow structures to accomplish data-driven tasks 
• Process text files in common formats like comma-separated values (.csv) files 
• Document and debug Python code 

Skills You’ll Gain

• Basic script writing
• Utilizing core Python language features and functions 
• Utilizing the interactive development environment Jupyter Notebooks 

Pave the path to a technical career by strengthening your programming skills. This online course is part of our Python for Data Analysis Professional Concentration and is designed for you to discover many of Python’s powerful programming features and apply them in hands-on exercises and assignments found in real world software applications.  

Intermediate Python will delve into essential data science applications of programming.  You will set up your own Python development environment like professionals do, including installing the best distributions of Python, creating your own virtual environments, installing packages, and connecting all of this to your own Integrated Development (IDE) software.  

Learning Outcomes

• Use IDE’s to create, run, test, and debug your code efficiently.
• Write programs using procedural and object-oriented programming techniques. 
• Write custom functions and add command-line interface capabilities. 
• Develop and master namescapes and scopes and understand how these impact variables and objects. 

Skills You’ll Gain

• Data analysis in Python. 
• Use of Panda. 
• Application of Object-Oriented Programming. 
• Advanced Python Programming tools including iterators, looping techniques, and generators. 

Unlock new career opportunities by delving deeper into the world of data science. This online course is the final part in our Python for Data Analysis Professional Concentration and is designed for professionals looking to upskill in Python programming and web development for use in programming, marketing and business analytics. 

Data Structures and Data Mining with Python will equip you with the foundational knowledge and hands on experience to build a portfolio through data engineering pipelines, data science workflows, and machine learning and deep learning operations for artificial intelligence application deployment. The course material is curated from top tech companies in the artificial intelligence sphere, allowing you to work with leading technology stacks from Amazon Web Services (AWS), Nvidia and Databricks on AWS. 

Learning Outcomes 

• Apply Python advanced language features to write efficient programs 
• Use advanced Python data structures for efficient handling of data 
• Use the RAPIDS computing framework for big data analysis and analytics 
• Use machine learning and deep learning concepts in AWS, Nvidia and Databricks on AWS 

Skills You’ll Gain 

• Data analysis in Python 
• Writing efficient programs 
• Using advanced tools in Python 

Get a head start in your career by gaining essential data analysis skills. This online course in our Python for Data Analysis Professional Concentration is designed for professionals looking to develop relevant analysis skills in general or switch to a more technical career.  

Python for Data Analysis will introduce basic data analysis applications of Python for those with little or no programming experience. Through remote lectures, group discussions and hands-on projects, you will learn how to ingest and clean data, plot basic graphs and fit regression-based models. Along with the basics of the Python language, you’ll get an introduction to various Python libraries, as well as learn how to import data and manipulate it efficiently using Pandas and NumPy, how to produce plots and data visualizations with matplotlib and how to run regression models using sci kit-learn. By the end of this course, you will be able to immediately apply your new skills to perform basic data analysis tasks in Python.  

Learning Outcomes  

• Read and explore data frames using various libraries including Pandas  
• Analyze and make recommendations from a dataset 
• Gain hands on experience testing a hypothesis using data  

Skills You’ll Gain  

• Basic data analysis in Python  
• Experience with Pandas, NumPy and scikit-learn 

Build a stronger understanding of foundational machine learning by using Python. This online course is designed for professionals looking to upskill as well as students and job seekers interested in learning the fundamentals of machine learning applications and data mining in Python. Skills gained in the course can be used across a broad array of industry roles, including marketing, business analytics, data analytics, and web and Python programming. 

Through online lectures, interactive assignments and projects, you’ll develop practical applications such as search engines, image analysis, bioinformatics, industrial automation, speech recognition and more. Examine machine learning concepts, gain a basic understanding of supervised machine learning and Bayesian classifiers, learn concepts in unsupervised machine learning and clustering algorithms and apply neural networks to machine learning. 

Learning Outcomes 

• Identify and formulate machine learning problems using Python 
• Understand and implement algorithms to solve simple machine learning problems 
• Analyze the performance of machine learning solutions on practical datasets 

Skills You’ll Gain 

• Usage of histograms for supervised machine learning and Bayesian classifiers  
• Design and application of classifiers like k-nearest neighbors, linear machines and decision trees  
• Unsupervised machine learning and cluster algorithms such as expectation maximization and k-means clustering