Welcome to this new series Career Prospects as a Student Developer where we take a better look into the different lines of specialization students consider when getting into college.
The idea of wanting to do this series came to me from my previous article About Choices as a Student Developer where I talked about my uncertainties regarding career path during the early months of college. So I just think this series might help me and others be more clear about what each specialization really has to offer and what really matters at the end of the day.
Alright now, let's get into the topic.
Topics we will be covering around :
- What is Data Science EXACTLY?
- What are the Learning Scopes ?
- How has Data Science influenced industry ?
- What is the future of Data Science ?
- Tools and Sites extensively used by Data Scientists.
What is Data Science EXACTLY ?
Well the above image seems overwhelming, and that's exactly what you need to get used to for anything really. Not the contents of the image but the feeling of being overwhelmed. And not letting that feeling stop you.
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning, deep learning and big data.
Well in more simple terms it is a set of disciplines that lets you as a Data Scientist handle data in a scientific manner. Yes, you get to call yourself a scientist or even a wizard maybe, you can choose one or the other. And you can use those skills either as a ~
Product Data Scientist : Which Involves being more versed in software development, Integration and ETL in addition to Data Science. This basically involves the integration of Data Science concepts on different applications to enhance their performance.
As a Business Data Scientist : Which Involves deriving insights for Business and Marketing in order to deduce the sections that boost the sales and quality of the company much more efficiently.
What is the Learning Scope for the Subject ?
While we keep hearing some great figures about the Data Science Salaries, and feel compelled to jump into the profession because of that, it is wiser to evaluate if the professions suits our skills and strengths. The Learning Scope for Data Science is particularly huge. While we do not need to know everything which of course no one can, we need to be able to pay very close attention to the concepts that determine the insights we derive from the problem that has been given to us. Data science involves, almost exclusively, heavy computation, math, and computer programming, day in and day out. A brief overview would look something like :
1. Getting Started with Data Science and Python:
At the start of the journey to becoming a data scientist, understand what a data scientist does, the various terms associated with data science, and we also need to start getting acquainted with the Python programming language
2. Statistics and Mathematics:
The BACKBONE of data science. Some people do get discouraged by even seeing the word statistics, I used to be one of those people. Some of the key concepts you’ll need to cover are probability, inferential statistics, and get a hang of how to perform exploratory data analysis (EDA). This will also include the basics of linear algebra (another core machine learning topic).
3. Machine Learning Basics:
You need to get acquainted with the basic Machine Learning algorithms and techniques, including linear regression, logistic regression, decision trees, Naive Bayes, support vector machines (SVM), among others.
4. Neural Networks (and Deep Learning):
Deep learning is part of the data science learning path. Given the rapid rise and adoption of deep learning applications, this is potentially a very relevant part for a data scientist. Learning about Neural Networks and being familiar with deep learning frameworks like Keras, Pytorch or TensorFlow
5. Computer Vision:
Computer vision is easily the most in-demand deep learning field in the industry. Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
6. Natural Language Processing (NLP):
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is what drives the recommendation engine of your YouTube Account.
How has Data Science Influenced Industry ?
While talking about career prospects, it is important to look at how the people who have come and gone from the Industry revolutionized it! Let's now look at some of the interesting things Data Science has put forward to the world :
1. Identifying The Pros and Cons of the Products more Effectively :
Data Science allows products to tell their story engagingly. Being able to identify the aspects in which the products rises or falls in effectiveness is a great feature that increases quality.
2. Better Education Analysis :
Teachers aren’t mind readers, so it’s hard for them to know how well their students understand a lesson. Even standardized tests have their shortcomings at measuring learning. Now data science is able to give teachers the insights they need. Data can be collected when students use school-issued computers or apps, which can then monitor their progress and share learning reports with teachers and parents. School districts also use data on a larger scale to find patterns in attendance and analyze schools’ performance throughout the district.
3. Applications in Financial and Insurance Sectors :
The finance and insurance sector is the clearest example of a data-driven industry. Big data represents a unique opportunity for most banking and financial services organizations to leverage their customer data to transform their business, realize new revenue opportunities, manage risk, and address customer loyalty.
4. Recommendation Systems for better User Experience
The entertainment industry has been transformed by the advent of streaming technology. Now companies like Netflix and Hulu are using the data they’ve gathered from consumers to provide highly personalized entertainment options, such as which shows to suggest you watch next.
“In the entertainment industry, companies are tracking user behavior on their websites and using A/B testing to improve user experiences,” Hatfield says. “This is not some once a decade or once a year endeavor, this analysis is now part of everyday customer relationship building.”
I have introduced only 4 topics that barely scratch the surface. The most interesting feature about Data Science is how flexible it has been in fitting into the different industries out there. From Education to Retail to Healthcare, Data Science has managed to make its importance prevalent in most of these.
What is the future of Data Science ?
Even when you account for the Earth’s entire population, the average person is expected to generate 1.7 megabytes of data per second by the end of 2020, according to cloud vendor Domo.
Yeah no kidding. The more the Data in this world, the more the importance of Data Science increases. A dominant theme today and going forward is that big data is going to play an influential role in the future. Data will define modern health care, government, finance, business management, marketing, energy and manufacturing. That means skilled talent will be needed across these industries to meet the challenges of data science to help innovate and improve the products. And Yes, Robots! But i really want to talk about those in a different article at length.
Tools and Sites Extensively used by Data Scientists :
The Data Science tools can be divided into different categories, but this list will mention only the most important ones :
1. Jupyter Notebook from Anaconda :
Jupyter Notebook is a popular tool to do all kind of things to data in an interactive environment (IPython). It supports Julia, Python and R as well. What I personally liked more about it is it's cell type switching system that just let's you make Markdown cell's right below the output of the program. Just feels better to be able to make inferences as I go with it. So a good tool for documentation as well.
2. Spyder from Anaconda :
Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It features a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package. Furthermore, Spyder offers built-in integration with many popular scientific packages, including NumPy, SciPy, Pandas, IPython, QtConsole, Matplotlib, SymPy, and more.
That was a hard definition. Anyway, I prefer Spyder over Jupyter because of it's better interface and for the ability to view the split datasets more flexibly.
3. Tableau/ Data Studio
Tableau and Data Studio are both Business Intelligence tools that help in building Dashboards for Data Visualization in a very elegant way. Data Studio is completely free and Tableau has Tableau Public for free. They can be used for freelancing projects or NGO use cases. The main motive being to be able to explain the clients about the insights from the data in an easy to understand way.
4. Kaggle
Kaggle is known as the dominating platform of data science competitions, public datasets, and Kernels sharing. It also offers learning resources as well.
Kaggle is also a go to place for data science-related topics so definitely consider more time in there.
5. Other Resources :
Well this includes some of the more obvious resources like
- Stack Overflow : where you can most likely get all your technical questions answered ( If the question is well structured ) unless they are excessively menial.
- GitHub : Where there's all sorts of projects to look into the source code and insights into as well as for uploading your own projects.
- Quora : For both technical and also casual questions. Just put out any relevant question about interviews, experience, work life balance, etc.
Conclusion
Alright I guess that is all. Hope this article helped you gain a good overview about Data Science. I am on the Data Science learning path myself so feel free to critique my points if you must, always great to learn from people in the field ! Signing Off, until next time.