Business Analytics, Data Analytics, and Data Science: An Introduction

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Business Analytics, Data Analytics, and Data Science: An Introduction

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The Data Science Course: Complete Data Science Bootcamp 2024

Complete Data Science Training: Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning

31:05:34 of on-demand video • Updated March 2024

The course provides the entire toolbox you need to become a data scientist
Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
Impress interviewers by showing an understanding of the data science field
Learn how to pre-process data
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
Start coding in Python and learn how to use it for statistical analysis
Perform linear and logistic regressions in Python
Carry out cluster and factor analysis
Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
Apply your skills to real-life business cases
Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
Unfold the power of deep neural networks
Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
English [CC]
Narrator: Okay. So, it's time for us to create a diagram that will shed light on the most popular disciplines in the data science field, and how they all intertwine with each other. As you know, everything is not particularly clear-cut, but the following diagram will help untangle the naughty mess of activities in the data science and business realms. Hold on to your hats. Let's begin with a list of terms, covering a few aspects of the business sphere. Business case studies, qualitative analytics, preliminary data report, reporting with visuals, creating dashboards and sales forecasting. Don't panic, we will explain the meaning of each of these terms posthaste. First though, take a moment to think what they all have in common. That's right, they are all part of the business world. We'll depict this with a blue rectangle here. Good. Now, let's think about the following. How many of these terms involve working with data? If we drew another rectangle and placed the activities for which it is essential to have data available in there, which ones, if any, would overlap with the business rectangle? The answer is some. That's because some business activities are data-driven, while others are subjective or experience-driven. Of course, there are disciplines that will overlap. We will place them in the common area of the two rectangles. So, let's see. Which of the subfields from our list represent a business activity involving data? Well, you'll need data to create a preliminary report, a visual representation of the performance of your company for last year, a business dashboard, and also a forecast for the future sales of your company, so these four labels can sit comfortably in the overlapping area. Great. The other two terms, business case studies and qualitative analytics, get left behind as they belong only to the sphere of business. They're not related to working with data. Let's explore why. Business case studies are real-world experiences of how business people and companies succeed or fail. You don't need a data set to learn from business cases, thus it will remain purely in the blue rectangle. Simple, right? The same can be said for qualitative analytics. As touched upon in the previous video, this is all about using your intuition and the knowledge of the market to help in the future planning process. Perfect, things are becoming clearer already. Now would be the perfect time to introduce a timeline to our graph. The reason for doing that is that some of the terms you see refer to activities that aim to explain past behavior, while others refer to activities used for predicting future behavior. Let's cut the picture roughly through the middle. This line will represent the present. Okay, therefore, all terms that are on the right side of this line will regard analytics, future planning and forecasting, and those that are on the left of the line will be related to the analysis of past events or data. Perfect! Business case studies examine events that have already happened. For instance, one could learn from them in attempt to prevent making a similar mistake in the future, right? As a result, we could say studying business cases is part of your analysis, so this activity refers to the past. Hence, it must be located on the left of the vertical line on our graph. We will move it up slightly to be able to contrast it to the other business term, qualitative analytics. This includes working with tools that help predict future behavior, therefore must be placed on the right. Awesome! In essence, what we have now is qualitative analytics which belongs to the area of business analytics, while learning from business case studies is part of your business analysis. However, this is not what many professionals would say. In practice, analytics has become a term comprising both analysis and analytics. Although this goes against what we explained earlier, please allow us to accept the industry jargon and leave business analytics as a label for the entire set of business processes you see on the graph. It may look like we're confusing matters, but things will be simpler in the long run. All right, hopefully, we're starting to get our heads around many of these terms and where they fit with each other. So, what about the rest of the terms you see on the diagram? Well, preparing a report or a dashboard is always a reflection of past data, so these terms will remain untouched. Forecasting though, is a future-oriented activity, so we can move it to the right of the black line, but not too much. It must still belong on the sphere of business, so it must be in the area where business analytics and data intersect. Great! Again, it is common practice to use the encompassing term, data analytics, to refer to both the analyses and analytics. While we know what the terms mean now, to keep things simple, following the standards of other business professionals is a good idea. Okay, good. Let's continue with adding another term. The most sparkly of them all, data science. Data science is a discipline reliant on data availability, while business analytics does not completely rely on data. However, data science incorporates part of data analytics, mostly the part that uses complex mathematical, statistical and programming tools. Consequently, the screen rectangle, representing data science on our diagram, will not overlap with data analytics completely, but it will reach a point beyond the area of business analytics. Okay. Great. Does this mean that the preliminary data report, reporting with visuals, creating dashboards and sales forecasting are of interest to a data scientist? Yes, absolutely. And now, do you think we can find an example of a discipline that belongs to the field of data science and is considered data analytics but not business analytics? Well, think of the oil and gas industry and the optimization of drilling operations, this is a perfect fit for this subarea. Data science can be used to improve the accuracy of predictions based on data extracted from various activities typical for drilling efficiency, and that certainly isn't business analytics. Great. What is left is to think of something that involves data analytics but neither data science nor business analytics. Can we find an example here as well? Sure, digital signal processing would do the job. Digital signal is used to represent data in the form of discreet values, which is an example of numeric data. Therefore, data analytics can be applied to digital signal in order to produce a higher-quality signal, and that's what digital signal processing is all about. Amazing! We hope the diagram is helping you to visualize how all the pieces fit together because we're not done yet. We will continue with the remaining disciplines related to the world of business and data science in our next video. See you there.