Introducing our new Data Scientist Learning Path

It is now estimated that 2.5 quintillion bytes of data is created every day globally, which is a phenomenal amount that could be used to help you make improvements across the business by:

• Supporting informed, data driven decision-making
• Simulating business outcomes based on potential actions
• Developing patterns using raw data by using tools, algorithms, formulas and machine learning
• Solving business problems and identifying new challenges to overcome
• And more…

Which is why data science is already fast-growing field with many career and business opportunities!

What will you learn on our Data Scientist Learning Path?

This learning path is structured in five steps, and then there are four electives – here’s an outline of each step:

Step 1 – Python for Data Science:

this step will provide you with an introduction so you can familiarise yourself with programming. It has been crafted by IBM to help you write Python scripts, use the JupyterLab environment to perform data analysis and create your own Data Science projects by utilising IBM Watson. By the end of this step, you’ll be able to:

• Write your first Python program by implementing concepts of variables, strings, functions, loops, and conditions
• Understand the nuances of lists, sets, dictionaries, conditions and branching, and objects and classes
• Work with data in Python such as reading and writing files, loading, working, and saving data with Pandas
• And more…

Step 2 – Applied Data Science with Python:

Python is a required skill for many Data Science roles, so this step is essential for starting your career, as it’ll teach you the essential concepts of Python programming. It will also provide you with in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing. On completion of this step, you’ll have the ability to:

• Install the required Python environment and other auxiliary tools and libraries
• Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions
• Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
• And more…

Step 3 – Machine Learning:

Machine Learning automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. During this step, you’ll learn about Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modelling to develop algorithms. At the end of this step, you’ll be able to:

• Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
• Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
• Validate Machine Learning models and decode various accuracy metrics
• And more…

Step 4 – Tableau Training:

this step will help you build visualisations, organise data, and design charts and dashboards. It’ll also teach you about Data Visualisation concepts, different combo charts, and stories, working with filters, parameters, and sets, and building interactive dashboards. After you’ve finished this step, you’ll be able to:

• Understand metadata and its usage
• Work with Filter, Parameters, and Sets
• Master arithmetic, logical, table, and LOD calculations
• And more…

Step 5 – Data Science Capstone:

the final of this learning path will help you apply your learned skills to solve a real industry-aligned Data Science problem. This Capstone course will also be based on the Data Science decision cycle, and these are the four project milestones:

• Data Processing
• Model Building
• Model Fine-tuning
• Dashboarding and Representing Results

After all steps have been completed in line with the required criteria, learners will receive a ‘Certificate of Achievement’ from Simplilearn and a ‘Python for Data Science’ certificate from IBM. As part of the learning path, learners will also have access to these electives:

• SQL Training
• Data Science with R Programming
• Deep Learning with Keras and TensorFlow
• Industry Masterclass delivered by IBM

How is the learning path delivered?

This Data Scientist Learning Path is delivered digitally using:

• Online self-learning: consisting of videos and quizzes
• Live interactive classes: via virtual classrooms with live interaction and monitoring
• Hands-on experience: through final assessment, project work and virtual labs

Want to learn more?

Visit ILX.com where you can download our brochure and book training