SAP Data Science & ML for SAP Consultants

Explore Python basics for data science and machine learning, using a cake recipe analogy to learn syntax, variables, inputs, outputs, control statements, loops, and libraries.

Define variables in Python by using the equals operator to assign values (e.g., a=10, b=5, c=a+b) and reference them in a Jupyter Notebook.

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.

Explore Python data types with numeric types - integers, floats, and complex numbers - and learn to classify them using type() in a Jupyter notebook.

Explore the Jupyter notebook interface hands-on, rename notebooks, navigate file and edit menus, and master keyboard shortcuts to run, insert, convert between code and markdown cells, and manage cells efficiently.

Explore boolean and sequence data types in Python, including strings, lists, and tuples, with practical examples showing how to assign, print types, and distinguish lists from tuples.

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.

Learn how Python dictionaries store key-value pairs inside curly brackets and how Python sets provide unordered collections for operations like union, difference, and intersection.

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.

Learn python operators, covering arithmetic operators (+, -, *, /, %, **, //), assignment operators, unary minus, and relational operators (>, <, >=, <=, ==, !=) with A=14 and B=5.

Learn to add Python comments, including single line using hash and multi line with triple quotes, to clarify code, and understand Jupyter’s interactive behavior and when to use print.

Learn how to add single-line and multiline comments in Python using hash and triple quotes, avoid name errors, and understand the interactive behavior of Jupyter Notebook cells.

Explore how the Python print function outputs variables and values, prints multiple items using commas, evaluates expressions before printing, and uses these features in a Jupyter notebook.

Master the print function by outputting variables, literals, and computations, combining values with commas, using print identifiers and dot format, and printing multi-line strings.

Learn how typecasting converts Python data types, including implicit casting during mixed-type arithmetic to prevent value loss, and explicit casting using built-in functions like int, float, and str.

Demonstrates implicit casting in Python by promoting integers to floats (and to complex when needed) to avoid value loss, and casting with int, float, complex, str, list, tuple, set conversions.

Learn to collect user input in Python with the input function, convert string values to numbers, and compute sums and averages from three numbers.

Study python control statements, including if, elif, else, for and while, and learn how indentation and boolean conditions govern program flow.

Explore how and, or, not operators enable Python to form compound conditions for if statements in SAP data science contexts, illustrated with examples and a Jupyter notebook.

Explore logical operators in conditional statements by building a program that finds the largest of three numbers x, y, z using and, or, and not in if conditions.

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.

Apply Python if-elif-else to classify an input number as positive, negative, or zero, and then map 0–9 to words, reporting invalid inputs.

Explore how while loops control repetitive execution in Python, using a condition to repeat a block until it becomes false, with an example counting from 1 to 5.

Learn to implement while loops in python to print 1–15, print even numbers 50–100, and list odd numbers within a user-provided min–max range using int input.

Master for loops to iterate over each element of a sequence, such as strings, tuples, dictionaries, and sets, executing statements for every element and contrasting with while loops.

Learn how to use Python for loops to iterate over strings and lists, print characters and values, compute sums, and nest loops to handle multi-dimensional data.

Explore break, continue, and pass in Python control statements, showing how break exits a loop, continue skips an iteration, and pass acts as a no-op with practical loop examples.

Learn Python control statements: break, continue, and pass, applied in for loops to manage execution and printing. Practice searching values in lists and handling user input.

Explore data structures in Python, including lists, tuples, dictionaries, and sets, and learn how to store and organize multiple data values for data science and machine learning.

Explore Python tuples, an immutable, parenthesized collection with indexed and sliced access; use len to count items, and learn why append or remove don’t exist, as shown with shop_tuple.

Explore hands-on Python tuples, including immutability and why you cannot delete or modify elements. Learn to use len, index access, and slicing with start, end, and step, including blank defaults.

Explore Python dictionaries: create and manipulate key-value pairs, access and modify elements, and retrieve keys, values, and items, using pop, del, copy, and clear in a hands-on Jupyter notebook.

Explore Python dictionaries through hands-on examples, including creating key-value pairs, accessing values by keys, listing keys and values, iterating items, and common operations like add, delete, copy, and clear.

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 sets with hands-on examples: create a set with name, age, and date of birth; add country and education using add and update, and check size with len.

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 Python strings act as collection objects, index from zero and slice with exclusive end, and use len, strip, upper, lower, replace, and split, with triple quotes.

Explore string methods like upper and lower for case conversion, replace and split, and use in and not in with concatenation via the addition operator.

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.

Explore Python functions by building an add two numbers function, demonstrating print vs return, default parameter values, positional and keyword arguments, and local scope.

Learn how to nest functions in Python by creating a function that computes sum and average using inner functions, and explore simple lambda examples.

Learn object oriented Python by exploring classes and objects, with attributes and methods acting as a blueprint for reusable, maintainable code—and the role of inheritance.

Explore Python's object oriented approach by building classes and objects, using init, self, attributes, and methods. Practice with examples like circle area and circumference and a hangman game case study.

Complete a capstone project using Python to build a hangman game, featuring a random word, letter guessing, and a runnable class-driven program.

Initialize a hangman game by selecting a random word, converting it to uppercase, building a letters list, setting a guesses list with ten max attempts, and displaying a star-based hint.

Implement and refine a Hangman game loop in Python, handling uppercase input, single-letter versus full-word guesses, guess tracking, duplicates, and remaining chances.

Discover how NumPy offers memory-efficient array objects and tools for multi-dimensional data. Install via pip or conda, verify with import numpy as np and dtype usage for integers, floats, booleans.

learn to install and import numpy, verify version 1.18.5, and use the NP shorthand to access array functions and explore help in a Jupyter notebook.

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.

Explore numpy array properties by using ndim, shape, dtype, itemsize, nbytes, and size to understand dimensions, data types, and memory usage when loading data and validating shapes.

Explore numpy array indexing and slicing to access and modify elements and subarrays across 2d and 3d arrays, using rows, columns, colon, and negative indexing.

Master numpy array creation using inbuilt functions like zeros, ones, full, full_like, random, identity, and repeat, with emphasis on shape and dtype.

Learn how numpy arrays share memory through assignments, causing A and B to reflect changes, and use copy to create independent arrays.

Create A (3x5) and B (5x4) in NumPy, perform matmul to obtain a 3x4 result, and explore the determinant, trace, SVD, eigenvalues, norms, and inverses.

Learn to modify numpy array shapes with reshape and resize, and stack arrays with vstack and hstack, noting reshape preserves data and resize may repeat elements.

Master the numpy arange function to generate numpy arrays with start, stop, and step, understand end exclusivity, and reshape arrays to 3x3 for data preparation.

Learn boolean masking in numpy to filter array values with relational operators and boolean indexing. See how true results form masks and prepare for broadcasting in the next video.

Explore broadcasting in NumPy arrays, learn how to align shapes using padding and stretching, and apply the three broadcasting rules with practical cases.

Explore the numpy library journey: import numpy as np, create arrays, inspect attributes (dimensions, shape, dtype, size), and use indexing, slicing, boolean masking, and broadcasting.

Explore pandas, a Python data analysis library built on NumPy that enables fast data analysis, cleaning, and preparation from CSV, Excel, JSON, and HTML sources using Series and DataFrame.

Learn to create Pandas series, a single-column data structure, from Python lists or NumPy arrays, assign a name and index, and access elements by label or position.

Create a Pandas series from a Python list with index labels, then add two series to show how mismatched indices yield NaN.

Explore how a pandas dataframe acts like table built from series with index labels, and create dataframes with pd.DataFrame or pd.concat to extract columns or rows with iloc and loc.

Filter a pandas DataFrame using boolean indexing and relational operators. Add new columns, drop columns or rows with inplace options, and reset or set the index to reorganize data.

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.

Explore advanced pandas dataframe operations, including applying custom functions via apply and applymap, column management, grouping, and exporting data to csv and excel.

Perform exploratory data analysis to discover patterns, anomalies, and test hypotheses. Load and clean the Uber New York City dataset, handle missing values, and prepare for predictive modeling.

Connect Google Colab to Google Cloud, load a CSV from a URL with pandas read_csv. Inspect with head, tail, and info to confirm 31 million rows and six columns.

Assess data quality with pandas by using duplicated() to find duplicates, isnull() with sum() for missing values, and identify unique origin and destination codes, with business input guiding handling.

Identify and report null values in the destination column of the dataframe, export a csv report for missing destinations, and remove rows where trip duration and distance are null.

Convert string pickup date time to date time with pandas apply, create a date hour column, drop the original column, and compute min and max to confirm a one-year range.

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.

Explore Matplotlib basics for data visualization in Python, learning static, animated, and interactive plots, figure and axis anatomy, styles with plt.style.use, and creating plots with plt.figure and plt.axis.

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.

Learn to customize Matplotlib plots for SAP data science and ML tasks by controlling colors, line styles, and markers, adjust axis limits and ticks, and add labels, titles, and legends.

Explore matplotlib plot customization by adjusting color, line style, markers, legends, titles, labels, and axis limits to design clear, multi-line visuals.

Explore scatter plots in Matplotlib by plotting pairs of points without connecting lines, and customize with plt.scatter or plt.plot, including color maps and marker size, demonstrated in a notebook.

Create scatter plots with Matplotlib by using plt.plot or plt.scatter, control marker styles, sizes, and colors, and apply color maps and legends to visualize x and y data.

Create histogram plots to show the frequency of numerical values using plt.hist and bin settings, then extend to dataframe hist with Pandas df.hist for all numeric columns.