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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.
Data Science Library Pandas
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1Setting up the Python Environment
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.
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2Python 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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3Closer Look at Python - Conditional Statements
In this exercise, we will look at the different ways of implementing conditional statements using IF statements
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4Closer Look at Python - For and While Loops, Break and Continue Statements
In this exercise, we will look at how to use For loops and While loops.
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5Python Language Reference
I briefly discuss a source for Python language reference that you can use to do your own deeper dive with Python.
Data Science Library Numpy
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6Introduction to 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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7Series Create List
Create a List from Pandas Series
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8Series Assign Labels
Adding custom labels to a column.
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9Series Slices
Print a range of column values.
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10Multiply and Exponential Series
A closer look at performing multiplication on an entire row of data. Multiply and Exponential.
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11Pandas Create Dataframe
A closer look at Pandas with DataFrame.
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12Accessing Column Values from a DataFrame
How to access a specific value from a column in a Dataframe.
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13Add and Delete a column of data to an existing DataFrame.
Will show how to dynamically add a column to a DataFrame.
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14Filter Rows and Performing Calculation to a DataFrame.
Show how to filter data from a DataFrame dataset and perform calculation on them.
Guideline When Building Machine Learning and Related Data Engineering Concepts
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15Introduction to 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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16Closer Look - One and Two Dimensional Array
A much closer look at Numpy working with one and two dimensional arrays.
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17Closer Look - Operations on One and Two Dimensional Arrays
This time we will perform operations on both one and two dimensional arrays.
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18Closer Look - Calculations on One and Two Dimensional Arrays.
In this exercise, we will look at how easy it is to perform calculations on both one and two dimensional arrays.
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19Closer Look - Reshape and Slice on One and Two Dimensional Arrays.
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.
Sentiment Data Analysis and Visualization Technique
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20Guideline 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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21Data Analytics and Approaches
In this lecture, we discuss the different categorical types of data analytics and the different approaches for each one.
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22Understanding 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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23Data 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.
Forecasting Data Analytics
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24A look at Bitcoin Sentiment Data
Implement preprocessing logic to divide a client data between training and test datasets.
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25Bitcoin 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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26Visualization Technique with Seaborn
In this exercise, we display the sentiment data using the Seaborn library along with the MatPlotLib library.
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27Closer Look at Bitcoin Tweets Raw Data
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
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28Pre-process Bitcoin Tweets - clean data
We will use data_process_bitcoin_tweets_data.py to pre-process or clean the data before aggregating sentiment.
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29Aggregate Tweets into Positive, Neutral and Negative
This exercise will take the cleaned bitcoin data and will iterate through each tweet and determine if it is positive, negative or neutral.
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30Graph Bitcoin Sentiment Results using Pie Chart with MatPlotLib
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.
Machine Learning Data Analytics by Example
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31Inventory 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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32Data Analytics - Inventory Optimization
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.
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33Inventory Optimization - Data load and Dependent Variables
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
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34Inventory Optimization - Calcuate EOQ (Economic Order Quantity)
We will add the calculation to get the Safety Stock level and at what point to re-order specific items.
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35Inventory Optimization - Visual Representation of EOQ Report
The final exercise for this data analytics is to display the EOQ results onto a graph using Seaborn.
Bonus Knowledge - Machine Learning with pySpark and Apache Spark in Databricks
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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