Workshops

This workshop series is specifically designed for professionals who do not have the extra time to earn a data science degree but are enthusiastic about learning data science by themselves. The workshop videos provide a quick understanding of Data Science, Machine Learning, and AI concepts.

Any professional with proficiency in basic Python programming will be able to go over the materials below easily. In these videos, I explain the theories briefly and then provide practical coding examples that learners can use directly in their work.

Even though a prerequisite of this workshop series is Python programming, I believe a professional with knowledge of any programming language can follow these workshop videos and materials.

All the workshop materials (including data, codes, and videos) are as follows. The list will keep growing as I make newer videos.

Data Science Workshop 1 (Part 1): Numpy, Pandas, scikit-learn

Hi I am Dr. Shahriar Hossain. I am running a data science workshop series for a small group of experienced professionals who have at least a computer science degree. Some of them have business and administration backgrounds. All of the participants have more than a decade of experience in the software and information technology industry. Most importantly, all of them ...
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Data Science Workshop 1 (Part 2): Numpy

Hi, I am Dr. Shahriar Hossain. Welcome back to Part 2 of Data Science Workshop 1. We barely started to discuss NumPy in the previous video. Let us begin from where we left in the last part. NumPy is an excellent package to keep datasets in the main memory. The Package has ample mathematical functions that are basic to many ...
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Data Science Workshop 1 (Part 3): Pandas

Welcome back. I am Dr. Shahriar Hossain. This is Part 3 of Data Science Workshop 1. In the previous part, we discussed NumPy. In this one, we will start to discuss Pandas. The Pandas library for Python is designed for data processing and analysis. You can read data from files, process the data, and write to files using Pandas. We ...
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Data Science Workshop 1 (Part 4): Pandas — Continued

Hi, This is Part 4 of Workshop 1. We are continuing the discussion on the Pandas library. The code of this part is available below. We are starting from where we left in the last part. Here is the YouTube video on Data Science Workshop 1, Part 4. Contents1 Creating a new column2 Removing a column from a Pandas DataFrame3 ...
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Data Science Workshop 2 (Part 1): What is Machine Learning?

We are starting Workshop 2. Starting from this video, we will be discussing machine learning topics using the Python programming language. As I said early in this workshop series, these videos come from workshops that I ran for experienced professionals in the software and information technology industry. Of course, for privacy reasons, I do not include the conversations I had ...
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Data Science Workshop 2 (Part 2): Machine Learning Jargon

Hi, This is Dr. Shahriar Hossain. I will go over an unsupervised learning algorithm using Python programming language soon. While explaining unsupervised machine learning to the audience, I figured that some terms are confusing for people who have just recently started to learn machine learning and data science. The most widely used terms are datasets, features, dimensions, attributes, and objects.  ...
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Data Science Workshop 2 (Part 3): k-means Clustering: An Unsupervised Machine Learning Algorithm

Hi, this is Dr. Shahriar Hossain again. Today, we will discuss an unsupervised machine learning algorithm and use the scikit-learn machine learning library. The concept I will discuss is called clustering. The specific algorithm that we will look at is called the k-means clustering algorithm. We will discuss what it is, and then we will write a python program. Knowing ...
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Data Science Workshop 2 (Part 4): Data and Cluster Visualization

In this part I discussed how we can visualize data points and clusters formed by those data points. The library we used is called Matplotlib. I imported the pyplot facilities of Matplotlib for the visualization of data points using Python. Here is the YouTube video will the details. The notebook file written in the video, using Google colab, can be ...
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Data Science Workshop 2 (Part 5): Data and Cluster Visualization (Continued)

As I was saying in the previous part, we will not get an interactive graphical interface if we use Google colab. That is why I am using regular Python on my computer. When I use Python, my program needs to display the figure on a canvas. Unlike directly trying to display fig on the notebook like what we do on ...
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Data Science Workshop 3 (Part 1): Exploratory Data Analysis using Pandas in Python Programming

Today, I will describe an initial way of exploring and analyzing data using Pandas in Python programming. Contents1 Exploratory Data Analysis2 Pecan Yield Dataset3 The Video4 The Notebook File5 Python version of the code Exploratory Data Analysis There is a process called Exploratory Data Analysis, which refers to the initial investigation of data. The phrase “the initial investigation” is a ...
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Data Science Workshop 3 (Part 2): Choosing the number of clusters

Today’s video discusses a way to find the optimal number of clusters, especially when we do not have any benchmark data. The question here is — Given a dataset and no supervision, how can we figure out what number of clusters, k, is giving us the best results? Contents1 The YouTube Video2 Dataset3 Clustering evaluation technique used in the video4 ...
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Data Science Workshop 4 (Part 1): Prediction using Linear Regression-Based Models

In the following video, we discuss prediction using linear regression-based models. I am confident that if someone watches the video till the end, the concept of prediction using linear regression will become crystal clear. Many fundamental questions might still remain unanswered from the time we studied statistics. In the video, we go over those questions using step-by-step, easy-to-understand examples. Here ...
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Data Science Workshop 4 (Part 2): Python Code for Linear Regression-Based Prediction

In this video, we start our Python code for prediction using linear regression. Linear regression is a simple but elegant prediction method. It works pretty well for many datasets despite its simplicity. It should be mentioned that there are some properties that the data must hold for a linear regression-based model. We did not cover any discussion on those properties ...
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Data Science Workshop 4 (Part 3): (Continued) Python Code for Linear Regression

Today’s video completes the Python code that we started in the previous video. In this video, we discuss how the actual linear regression model is built and then used for prediction. Here is the video. The Pecan.csv file and the code You can download the Pecan.csv file from this link: Pecan.csv. Please note that Pecan.csv contains synthetic data. It contains ...
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