
Launch an IPython notebook in a Jupyter session by selecting Python 3, write print Hello world, and run to confirm installation and start the machine learning journey.
Explore Python variable creation by applying rules: start with a letter or underscore, allow alphanumeric names, honor case sensitivity, and avoid reserved keywords; learn through examples, printing, and basic syntax.
Learn boolean values true and false in Python, and explore sequential data types such as strings, lists, and tuples, including multi-line strings with triple quotes and type checks.
Explore Python data types by building dictionaries with key-value pairs of various data types and creating sets to store unordered collections, with hands-on exercises on real-world keys, values, and numbers.
Explore the if else control statement, executing code blocks when conditions are true or false. Learn through examples of even/odd checks, between 10 and 50, and three-number sums.
Explore Python sets, unordered collections of unique elements enclosed in curly braces, and learn to add, update, and remove or discard items, and perform union, intersection, and other set operations.
Explore Python set operations including union, intersection, difference, and subset and superset checks, plus remove and discard behaviors, for SAP data science workflows.
Learn how to use negative indexing and escape characters in Python, accessing list elements from the end and applying backslash escapes for new lines and tabs.
Learn to create n-dimensional numpy arrays with np.array from Python lists and tuples, cast data types (int, float), and build multi-dimensional arrays while inspecting array attributes.
Learn how numpy arrays share memory through assignments, causing A and B to reflect changes, and use copy to create independent arrays.
Learn to combine multiple data frames in pandas using concat, merge, and join, with SQL-style concepts like outer, inner, left, and right joins, and create frames from Python dictionaries.
Explore additional Pandas dataframe functions, including info, unique, value_counts, and mean, and learn boolean indexing with multiple conditions, as well as and or operators and column operations.
Learn data visualization concepts and how to use matplotlib and seaborn to turn tabular data into visuals. Gain quick insights, spot trends, and tell compelling stories for higher management.
Learn to create line plots in Jupyter using Matplotlib, from setting up figures and axes to plotting sine and cosine waves, with static and interactive options.
Unlock a New Era in Your Career: Data Science with SAP - Machine Learning for Enterprise Data
Welcome to a transformative learning journey designed to bridge the gap between SAP Professionals and Data Scientists. As you embark on this course, you'll discover the striking similarities between the activities performed by Data Scientists and the way SAP Professionals implement business requirements on ERP Software - SAP. The pivotal distinction lies in the Data Scientists' knack for posing more insightful questions about the data they encounter.
Our curriculum, meticulously crafted for SAP Professionals venturing into the realm of Data Science, covers a diverse range of essential topics:
Understanding the Data Science Field and Types of Analysis:
Gain insights into the fundamental principles and practices of the data science field.
Explore various types of analysis that form the backbone of effective data-driven decision-making.
Statistics:
Delve into statistical concepts, empowering you to make informed decisions based on data-driven insights.
Python:
Acquire proficiency in Python, a versatile programming language extensively used in the data science landscape.
Advanced Statistical Techniques in Python:
Apply sophisticated statistical techniques using Python, enhancing your analytical capabilities.
Data Visualization:
Master the art of visualizing data to convey meaningful insights through compelling graphics.
Machine Learning:
Dive into the dynamic world of machine learning, understanding its principles and applications.
Using Pretrained Models:
Leverage the power of pretrained models, including the Google Cloud Natural Language Processing API, for a seamless jumpstart in SAP Application implementation.
Each topic seamlessly builds upon the previous ones, providing a structured and comprehensive learning path. To ensure a smooth and non-overwhelming experience for learners, we recommend acquiring these skills in the specified order outlined in our curriculum.
In our pursuit to create the most effective, time-efficient, and business case-driven data science training online, we proudly present our course: "Data Science with SAP - Machine Learning for Enterprise Data."
Why Choose Our Course?
Holistic Approach:
We address the unique challenges faced by SAP professionals entering the data science field.
Our curriculum covers a spectrum of topics that smoothly flow and complement each other, easily connecting to Enterprise Data SAP.
Cost-Effective and Time-Efficient:
Access everything you need to become a data scientist from an SAP Consultant at a fraction of the cost of traditional programs.
Save valuable time with our efficient and focused learning path.
Comprehensive Package:
Enroll in our $3000 data science training program that offers a wealth of knowledge and practical insights.
Benefit from active Q&A support, community collaboration, a certificate of completion, and access to future updates.
Real-Life Business Cases:
Engage in solving real-life business cases that seamlessly translate to your practical application in the business domain.
Community Support:
Join a thriving community of data science learners, fostering collaboration and shared growth.
Risk-Free Learning:
We are confident in the excellence of our course content and offer an unconditional 30-day money-back guarantee. No risk for you!
Why Wait? Seize the Opportunity Now!