Fundamentals of Python, Machine Learning for Consultants
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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.
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1
Setting up the Python EnvironmentIn 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.
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2
Python Basics - variables, data types, operations and functions
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.
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3
Python Language Reference
I briefly discuss a source for Python language reference that you can use to do your own deeper dive with Python.
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4
Python Data Science Library Pandas
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.
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5
Python Data Science Library Numpy
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.
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6
Guideline When Building Machine Learning
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
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7
Understanding the Architectural Layers of Data Engineer
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.
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8
Data Preparation as an Example
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.
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9
A look at Bitcoin Sentiment Data
Implement preprocessing logic to divide a client data between training and test datasets.
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10
Bitcoin Sentiment Visualization Technique using Matplotlib
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.
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11
Visualization Technique with Seaborn
In this exercise, we display the sentiment data using the Seaborn library along with the MatPlotLib library.
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12
Inventory Forecasting - Probability Analysis
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.
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13
Linear Regression Data Analysis
In this lecture, we will implement a simple linear regression example and display to users with Seaborn chart.
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14
Clustering Data Analytics
In this lecture, we look at implementing an unsupervised Clustering of sales inventory to their discounted prices.
