Data Science and Business Buzzwords: Why are there so Many?

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The Data Science Course 2022: Complete Data Science Bootcamp

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

29:15:43 of on-demand video • Updated December 2021

  • 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 [Auto] What's so important about data in this day and age, maintaining a healthy business goes hand in hand alongside working with data, whether you understand it or not, there is no denying that data is at the foundation of any successful company. And the business entrepreneurs that are leading the way are aware that looking deeper into data is what will make them tower above the competition. Let's start with the data team or the big data team. They will want to solve a business problem. The team will do a significant amount of work on the data that is available first. Based on that, the business intelligence team will provide a business insights dashboard. After the dashboard is ready, the data science team will use some business analytics or data analytics tools to develop models that could predict future outcomes. Hold the phone. What on earth are we talking about? Unless you are a data science whiz kid. You may think we're just picking words out of the dictionary and sticking them together at random. Nope, these are actual data science buzzwords. And not only that, there are many other similar phrases. No wonder you're confused, but fret not. It's completely understandable for you to feel like this. Let us shed some light as to how things became so complicated. One cause of this confusion is the constant evolution of the data science industry and in turn, the meaning of these buzzwords. This complicates the situation a lot. For example, someone who had the title of statistician twenty five years ago would have been responsible for gathering and cleaning data sets and applying various statistical methods to the data. After some years, however, with the growth of data and the radical improvement of technology, this statistician would now be required to extract patterns from data. Henceforth, a new buzzword was coined data mining. Similarly, forward wind, a few more years in the same statistician due to new mathematical and statistical models could now perform more accurate forecasts. And again, another term has found its way into an already inflated business glossary, predictive analytics. Has the statistician changed her job at this point? Nope. Are her goals different? Nope, not really. However, she is more qualified now to be part of the statistics department, predictive analytics team or have the title data scientist. Hopefully it's easy to see now how these buzzwords develop over time and how someone who would qualify as a statistician 25 years ago and had kept up with modern technologies could fit into a multitude of professional categories. Now, interesting, but a little messy, right? Another cause of confusion, which stems from the one just mentioned, comes from H.R. managers who understandably can become overwhelmed with the barrage of new terms and buzzwords flying around. This causes them to label job positions inaccurately, often seeming like they are choosing them on a whim. One H.R. representative may call a job position data analytics specialist when in fact they need a data analyst. Another May employee, junior data scientist, when they require a business intelligence analyst, of course, there are many companies that word their job offers brilliantly, but this is not standard across the board, which can cause even more of a mess. Now, as exemplified already in the world of data and data, science can seem overwhelming and may very well make you want to run away and hide from anything data related. But bear with us a little longer. We, the 365 data science team, are here to help you sort through the clutter and see data science in a whole new, brighter, clearer light. We have designed a unique infographics to put everything together. Of course, you could search the Internet for different glossaries, but why waste time with us? You will find an aggregated, concise and to the point structure containing all technical and business terms that are frequently used in the field of data science. We begin by clarifying the similarities and differences between the terms business analytics, data analytics, data science, business intelligence and machine learning. Then we focus on helping you digest the definitions you need to know in an effective way. By the end, you will be more than capable of being able to relate and apply various expressions and buzzwords to the areas of data science they belong to. So what are we waiting for? Let's dive straight in.