What is Data Science?
Data Science seems really exciting but first, allow us to get your basics clear! What actually is data science?
Statistics, Computing, and specific Domain Knowledge all these are an amalgamation of Data Science. Statistics and computing are the generic fundamentals which will be perfected by studying and a little bit of practice. it's the domain knowledge that takes time, research, and energy to learn.
You don’t need to master each vertical but having an honest grip on all will assist you by the end of the day. Data Science is sort of an enormous field in itself. It starts with simple data reporting activities to advanced predictive modelling using AI. As you'll observe by watching the info science spectrum below, the higher the complexity the more is its business value. Data science is thrilling! Now, let’s check out the particular role of a knowledge scientist.
What does a knowledge scientist role look like?
Caution: These terms are loosely utilized in the industry; the precise role can depend upon the maturity of your organization in data initiatives.
• Understanding the matter statement – Seems really simple, right? Believe me, it isn’t. Understanding the matter statement is going to be the make-or-break situation for the entire duration of the project. At this stage, A team of knowledge scientists and therefore the concerned team re-evaluate the objectives and expected requirements of the project. It requires good communication skills and stakeholder management for this step. An honest data scientist won’t hesitate to spend an ample amount of your time on this step. Once the matter statement is obvious, the scientist can advance to the gathering of knowledge.
• Gathering Data – Once the desires are obtained and therefore the hypothesis formed, the info scientist then proceeds to mine the needed data. The source of the info can vary like a company data warehouse, web scraping, and so on.
• Data Cleaning – This is often the foremost time-consuming process of the whole data science project. it's going to take up to 80% of the time. Here, the scientist is going to be munging, manipulating, and wrangling the info. The time and energy are worthwhile since the health of your data will reflect the health of your output model. During this stage, the scientist deals with outliers, missing data values, correcting the data types, and lots of other operations. This is often not the foremost exciting step but the foremost essential one.
• Exploratory Data Analysis (EDA) – It's basically the step where the scientist gets the “feel” of the data. it's at this stage that you simply can analyze each feature or multiple features within the dataset and check how they behave. You'll also analyze the connection of features with other features. you'll expect tons of knowledge visualization at this stage.
• Feature Engineering – Feature engineering isn't such a lot of a step but an art. it's an iterative process, going one by one through all the features and applying operations to enhance the performance of the model. For instance, you'll combine a number of strong features and check out to enhance the model. It'll require tons of trial and error.
• Model Building – Model building in itself is comparatively a quick step but planning is vital. Does one need a model with high accuracy or a model which will return the importance of features? You got to think upon and choose your strategy for model building and its evaluation.
• Deployment – Once you've got built and evaluated your model, it's finally time to deploy it within the world. This step typically requires the info scientists to figure with data engineers or machine learning engineers
Problems solved by data scientists
As we discussed within the earlier section, the role of a data scientist has relevancy to all the fields and departments than are its applications. During this section, we will be discussing a few of problem statements that a knowledge scientist works upon.
• To build a model to predict which transaction is fraudulent
• Requires real-time decisions on fast-flowing data
• Complex problems since 99%+ transactions aren't fraud
• It has an immediate impact on rock bottom line of the organization
• A vast amount of past customer behaviour data is employed
• Use Vehicle images from accidents to assess the extent of the damage for an insurance firm
• Extracting damage information from images may be a highly complex task
• It requires automation of the task
• Automation will help the present team to assess the damage better
• A vast amount of image data is necessary and required
• These are a couple of problem statements and may vary consistent with the info maturity of the organization
The field of Data Science is growing at an exceptional rate and has a lot of scope for further growth if you decide to dive into it. Now, as you can see what the field holds for you, the syllabus of Data Science can vary in different colleges even if the core subjects stay the same. So, if you wish to pursue Data Science courses and are confused about how to go about it, let the counsellors at GICSEH help you make the right decision and shortlist the best colleges for you.
Join GICSEH today!!