Fundamentals of Python, Machine Learning for Consultants

Name: Fundamentals of Python, Machine Learning for Consultants
Rating: 4.7 (23 reviews)

Guide to Provide Professional Consultation for Machine Learning Projects

Created byJayper Sanchez

Last updated 4/2024

English

What you'll learn

Business or IT consultants looking to integrate machine learning into their problem-solving toolkit.
Professionals aspiring to transition into a data science or analytics consulting role.
Individuals with basic programming knowledge interested in applying Python in business analytics and machine learning.
Data analysts or junior data scientists aiming to enhance their consulting skills in client-facing roles.

Course content

8 sections • 41 lectures • 3h 29m total length

Setting up the Python Environment2:37
In this video, I briefly talk about what a developer needs that will be used throughout the entire course. I will briefly discuss the development environment and other tools and languages that is required for our development. I will not go through the exercise of actual installation as it is expected that the student have enough experience with software development environment setup.
Python Basics - variables, data types, operations and functions10:28
In this section, We will go over the basic, bare bone syntax of a Python application. The Python application will illustrate defining a function and how the syntax will look like and how a for loop and conditional statement syntax and format looks like.
Closer Look at Python - Conditional Statements3:24
In this exercise, we will look at the different ways of implementing conditional statements using IF statements
Closer Look at Python - For and While Loops, Break and Continue Statements5:26
In this exercise, we will look at how to use For loops and While loops.
Python Language Reference1:43
I briefly discuss a source for Python language reference that you can use to do your own deeper dive with Python.

Introduction to Pandas6:25
In this discussion, we are going to look at the tone of Python's library that is widely used in Python applications and for data analytics. In this lecture we will have a quick look at Pandas.
Series Create List3:03
Create a List from Pandas Series
Series Assign Labels2:24
Adding custom labels to a column.
Series Slices4:00
Print a range of column values.
Multiply and Exponential Series3:58
A closer look at performing multiplication on an entire row of data. Multiply and Exponential.
Pandas Create Dataframe2:58
A closer look at Pandas with DataFrame.
Accessing Column Values from a DataFrame3:37
How to access a specific value from a column in a Dataframe.
Add and Delete a column of data to an existing DataFrame.3:24
Will show how to dynamically add a column to a DataFrame.
Filter Rows and Performing Calculation to a DataFrame.3:01
Show how to filter data from a DataFrame dataset and perform calculation on them.

Introduction to Numpy4:18
In this discussion, we are going to look at the tone of Python's library that is widely used in Python applications and for data analytics. In this lecture we will have a quick look at Numpy.
Closer Look - One and Two Dimensional Array3:36
A much closer look at Numpy working with one and two dimensional arrays.
Closer Look - Operations on One and Two Dimensional Arrays5:26
This time we will perform operations on both one and two dimensional arrays.
Closer Look - Calculations on One and Two Dimensional Arrays.4:07
In this exercise, we will look at how easy it is to perform calculations on both one and two dimensional arrays.
Closer Look - Reshape and Slice on One and Two Dimensional Arrays.4:44
In this exercise, we will look at how to add new column, altering the arrays without having to change values using Reshaping. We will also look at slicing where this function allows us to select a specific row in an array.

Guideline When Building Machine Learning4:27
Understand client’s goal
Examine client’s data: volume, format and how it is gathered
Determine outcome from ML result that the client is looking for
Data Analytics and Approaches3:48
In this lecture, we discuss the different categorical types of data analytics and the different approaches for each one.
Understanding the Architectural Layers of Data Engineer9:27
In order for a machine learning to be implemented, there must first be data. Understanding how these data are prepared is important for the consultant.
Data Preparation as an Example6:22
In this lecture, I will show a typical scenario of what data preparation may look like when the consultant is onsite looking at client's data.

A look at Bitcoin Sentiment Data1:32
Implement preprocessing logic to divide a client data between training and test datasets.
Bitcoin Sentiment Visualization Technique using Matplotlib11:26
With the dataset divided between training and test data, we will use MatPlotLib to display the training or even test data in a visual graphically interface with the MatPlotLib library.
Visualization Technique with Seaborn11:48
In this exercise, we display the sentiment data using the Seaborn library along with the MatPlotLib library.
Closer Look at Bitcoin Tweets Raw Data1:32
In this exercise, we will have a look at a raw data that contains bitcoin tweets. We will then create a pre-processing to clean the data in preparation to aggregate sentiments. We will look at the bitcoin_tweets.json file and examine it further
Pre-process Bitcoin Tweets - clean data4:50
We will use data_process_bitcoin_tweets_data.py to pre-process or clean the data before aggregating sentiment.
Aggregate Tweets into Positive, Neutral and Negative4:11
This exercise will take the cleaned bitcoin data and will iterate through each tweet and determine if it is positive, negative or neutral.
Graph Bitcoin Sentiment Results using Pie Chart with MatPlotLib3:56
This exercise will take the results from the aggregated tweets and display to with Pie chart. We will use MatPlotLib for our charting. While still using aggregate_tweets_sentiments.py, we will now chart the results using Pie chart.

Inventory Forecasting - Probability Analysis11:26
In this first data analytics exercise, we cover probability and show the results in a graph using Seaborn. The use case is to determine the probability of products being sold out within a month and within the next three months.
Data Analytics - Inventory Optimization2:35
We will look into another data analytics that optimizes inventory of a specific store. We will first look at the data that will be used to perform this analytics.
Inventory Optimization - Data load and Dependent Variables5:41
In this exercise, we will begin with implementing loading of the data needed to perform this analytic. Then we discuss the data that is required for calculation to optimize inventory
Inventory Optimization - Calcuate EOQ (Economic Order Quantity)4:12
We will add the calculation to get the Safety Stock level and at what point to re-order specific items.
Inventory Optimization - Visual Representation of EOQ Report2:58
The final exercise for this data analytics is to display the EOQ results onto a graph using Seaborn.

What is Databricks and setup Cloud Account3:09
A look at Databricks cloud service with Spark
Load Data into Cluster7:05
In this lecture, we will load the data extracted from CoinGecko that has exchange information: date, open_price, closing_price and volume. This will then be used for data analytics in the following lecture.
Linear Regression with pySpark and Apache Spark8:36
We will create a linear regression from the SOL Exchange data and create a linear regression analytics using pySpark and Apache Spark that will handle the data load.
Where to go From Here3:33
A conclusion of the course and suggestion where to go from here.

Requirements

Basic understanding of programming concepts.
Basic understanding of development tools such as Visual Studio Code
Familiarity with elementary statistical concepts.

Description

This course is meticulously crafted to equip consulting professionals with a robust set of competencies in Python and Machine Learning, aiming to bridge the gap between theoretical knowledge and real-world application. It delves into the practical utilization of machine learning methodologies to dissect and address complex business challenges, ensuring consultants are not just consumers of analytics, but architects of innovative solutions. Beginning with a thorough grounding in Python programming, participants will master the language's syntax, libraries, and data structures, establishing a solid foundation for the more advanced topics to follow. As the course unfolds, it introduces the rich landscape of machine learning, from supervised and unsupervised learning to the latest in deep learning technologies. Participants will engage with hands-on projects that simulate actual consulting scenarios, applying algorithms to unearth insights, predict trends, and craft strategies that align with business objectives. The curriculum is infused with case studies and examples that resonate with the consultant's role, emphasizing the translation of technical results into actionable business strategies. By the end of this journey, learners will not only understand the mechanics of machine learning algorithms but also how to harness the power of Python to transform data into a compelling narrative for stakeholders. They will emerge as invaluable assets to their firms, capable of leveraging analytics for competitive advantage. This course doesn't just prepare consultants to meet the industry's demands; it empowers them to become thought leaders who can navigate the complexities of a data-driven marketplace with confidence and foresight. This comprehensive program ensures that by its conclusion, participants will have a portfolio of projects to demonstrate their expertise and a deep understanding of how machine learning can be a catalyst for innovative problem-solving in the consulting domain. Join us to embark on a transformative learning experience that will elevate your consultancy practice to new heights.

Who this course is for:

This course is designed to empower consultants with the essential skills and knowledge in Python and Machine Learning. It focuses on practical applications of machine learning techniques to solve real-world business problems. Starting with the fundamentals of Python programming, the course progresses into the intricacies of machine learning, enabling learners to effectively utilize Python to analyze data, build predictive models, and make data-driven decisions in a consultancy context. This course is not an introduction to Python programming. It assumes that you already have industry experience in the Software Development field.

Fundamentals of Python, Machine Learning for Consultants

What you'll learn

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Course content

Introduction to Python for Data Science5 lectures • 24min

Data Science Library Pandas9 lectures • 33min

Data Science Library Numpy5 lectures • 22min

Guideline When Building Machine Learning and Related Data Engineering Concepts4 lectures • 24min

Sentiment Data Analysis and Visualization Technique7 lectures • 39min

Forecasting Data Analytics5 lectures • 27min

Machine Learning Data Analytics by Example2 lectures • 18min

Bonus Knowledge - Machine Learning with pySpark and Apache Spark in Databricks4 lectures • 22min

Requirements

Description

Who this course is for: