Data-Science

Best Data Science Courses (2020) for Finance & FinTech

In FinTech by Gaurav SharmaUpdated On:

Data-Science

Data Science is a rather broad filed and includes all the processes, techniques and methods used to extract useful insights and information from raw data. Data analysis, programming, data visualization, predictive analytics using data, etc. are all examples of applications within the broader field of Data Science.

The financial services industry has always been one of the heaviest users of data. Mountains of data passes through the global financial systems each minute. This could be trading data, transactions, risk metrics, fraud signals or any number of such things. No wonder then that the demand for data science specialists is sky rocking at banks and other financial institutions.

The opportunities in this field are endless, the compensation is good and career prospects are bright. However, it is a field that has a high skill bar. You need to demonstrate some solid data science skill to get in – especially at the top tier firms that get a lot of applicants for each job.

Which is why you need to up your game and lean some core skills. This is a cherry-picked list of the best data science courses and certifications that will not only help you learn the stuff, but also add some serious firepower to your CV. Remember that your CV needs to stand out just as much as you do.


1. Data Science Specialization from John Hopkins University

Why take this course?

  1. This is a great beginner level course although some familiarity with Python and Regression is required. The course is designed to you through the entire data science pipeline from beginning to end. At the end of the program, you would have everything you need to thrive in a real-world data science environment.
  2. The course starts with some basic introduction to the tools of the trade including things like version control, markdown, git, GitHub, R, and RStudio. From there, you move on to programming R and using R for data analysis. Everything you need to know about R for statistical data analysis is covered with examples. The specialisation also focuses on collecting, cleaning, and sharing data which is an integral part of what you would be required to do in a real-life setting. The development of complex statistical models and exploratory data analysis is another focus.
  3. The second half of the specialisation focuses on statistical inference, regression models, machine learning and data products. Data products essentially automate data analysis and will prove to be helpful tool in your arsenal. The machine learning part has a rather practical bend to it which is something I found appealing.
  4. Everything is capped of by a capstone project to cement your learning. You earn a formal certification at the end of the course which will further boost your career prospects and boost your Resume. John Hopkins is a great brand to have on your CV so this should really help you stand out.

Summary

  • Time to Complete: Around 300 hours. This will take you a few months.
  • Available fully online and on-demand.
  • Recommended for beginners who want to learn everything there is to know about data science.

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2. Data Scientist Nanodegree form Udacity

Why take this course?

  1. Another excellent and compressive course covering everything about what it takes to be a data scientist. This is an intermediate level course so some understanding of python and mathematical concepts like probability and statistics is required.
  2. The course starts off with a deep dive into data visualization and communication. Then you move on to software engineer for data scientists and creating unit tests, building classes, running pipelines, transforming data, building models and so on. Experimentation and testing methodologies like A/B testing are then evaluated.
  3. Everything is capped off by a real world, open ended data science project. Udacity also provides value added services like technical mentor support and even access to a career cosh. So this really is a comprehensive program and picks you up and delivers you where you want to be – a qualified data scientist.

Summary

  • Time to Complete: Around 4 months.
  • Available fully online via Udacity’s excellent platform.
  • Recommended for those with some experience and looking to get into advanced data science roles.

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3. Data Science for Executives from Columbia University

Why take this course?

  1. You would be hard pressed to find a bigger brand than Columbia University on your Resume which is why this course was an instant recommendation for this list. Of course, the quality of the course matches the brand as well and if you are a banker or senior level executive interested in data science, this is the best option by far.
  2. As mentioned, this is a course meant for executives which means you look at the bigger picture rather than the nitty gritty of things. How data science is used in decision making, a practical understanding of the fundamental concepts including statistical thought, conditional probability, machine learning and algorithm, data visualisation, natural language processing, audio and video processing, the role of Internet of Things in data science and so on.
  3. Take this course if you want to be a leader in data science. If you want to lean your team or organisations with a fair understanding of data science is and what it can achieve. Most organisations are heavily using data science at some level and the idea behind such courses is to prepare managers to be ready to lead the change and speak the language of the data scientists. It is an indispensable skill to have in today’s environment.

Summary

  • Time to Complete: Should take around 100-115 hours.
  • Available fully online and on-demand.
  • The best brand you can have on your Resume.
  • Recommended for bankers, executives and others in managerial or leadership positions.

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4. Data Science for Finance Professionals from NYIF

Who should take this?

Recommended  for Quants and Financial Engineers.

Why take this course?

  1. This course has been custom built for finance professionals by the New York Institute of Finance. NYIF is an institute that is dedicated to financial education, so you are unlikely to find a more finance-centric course on the subject. It is also the best branding available form a CV enhancement perspective if you want to apply for a quant type role at a bank or a fund.
  2. This course has been designed for financial engineers, quants, traders, developers in the financial services industry, data scientists, tech savvy portfolio managers and others who want to utilize machine learning in the financial services industry. There is a lot of focus on ML algorithms.
  3. After looking at some of the Python basics and learning how to manipulate and analyze arrays, you move on to the machine learning stuff. What I like about this course is that it is just as comprehensive as the other courses on this list, but it manages to compress that into just a few intense days which should suit busy professionals. I personally would prefer one week of intense study over 8 months of regular low intensity classes.
  4. This is not a basic course and you will need to use python, calculus, algebra as well have a basic understanding of financial instruments. This is a more advanced course that will take your existing Python skills to the next level form a machine learning perspective. It is highly recommended you brush up on these concepts before starting.

Summary

  • Time to Complete: About 5 days of intense virtual classroom learning.
  • Available fully online and on-demand. Complete at your own pace.
  • Hyper focused on finance so ideal for those who already know that AI applications in finance is where they belong!
  • This is an advanced course, so some preparation is likely needed (Python, algebra, finance basics).

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5. Applied Data Science with Python from University of Michigan

Why take this course?

  1. Another great course but this one is designed for intermediate students who already have some experience with python or other such programming skills. You will lean to apply statistical, machine learning, data visualisation and other techniques to analyse data. You will be armed with python toolkits such as pandas, matplotlib, scikit-learn, nltk, and network.
  2. The initial few weeks focus on python fundamentals like lambdas, reading and manipulating csv files, and the numpy library. Next, you will focus on data visualizations mostly using the matplotlib library -applied plotting, charting and data representation.
  3. The second half of the course focuses on applied machine learning and introduce the scikit learn toolkit. Lastly, you will deep dive into text mining and social network analysis. Everything has a practical bend to it and is approached as an applied science rather than a theoretical one. Libraries like NetworkX are used for network analysis while the nltk framework for manipulating text.
  4. This is an excellent course for learners who already a bit of experience but need to learn more to progress. A course completion certificate is awarded at the end and the University of Michigan is another great brand on your CV.

Summary

  • Time to Complete: Around 120 hours.
  • Available fully online and on-demand.
  • This is an intermediate course that requires some programming experience with python.
  • Recommended for professionals who want to move into more advanced data science roles.

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6. Professional Certificate in Foundations of Data Science from UC Berkley

Why take this course?

  1. Another excellent course with excellent CV brand value. The course is well suited for newcomers to the filed as it provides a friendly yet comprehensive look at data science. This is a foundational course which mean it will give you the basic tools to develop computational approach to problems and inferential thinking. These are critical skills for anyone in today’s data driven business environment.
  2. The course will teach you to interpret and communicate data and results, make predictions using machine learning and statistical methods and make decisions based on available data. It will also teach you computational skills including suing Python for data analysis and visualisation.
  3. A third of the course focus on machine learning and predictions with a focus on regression and classification for pattern analysis and predictions. It is a solid conclusion to a practical, yet approachable look at machine learning and data science.
  4. UC Berkeley is an most definitely a brand that you want on your Resume and that just adds a cherry on top of an already great course. Don’t miss this opportunity if you are interested in breaking into data science.

Summary

  • Time to Complete: Around 70 hours.
  • Available fully online and on-demand. Complete at your own pace.
  • Recommended for beginners and generalists who want to develop a foundational understanding of the core concepts.
  • UC Berkley provides excellent brand value.

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7. Data Engineering with Google Cloud Professional Certificate

Why take this course?

Google Cloud Platform (GCP) is a big deal in data science and this course will go a long way in helping you master it.  You will lean to make data driven decision using data that you collect, transform and publish using advanced techniques.

The course starts with a introduction to the Google Cloud Platform followed by data warehouses, data pipelines, streaming analytics, machine learning and so on. It essentially prepares you for the Google Cloud Professional Data Engineer Exam by providing you with some great practical experience.

This is an intermediate level course which does require some prior experience with SQL. As such , it is recommended for somewhat experienced techies who are interested in the google Cloud Platform.

Summary

  • Time to Complete: Around 60-65 months.
  • Available fully online and on-demand.
  • Recommended for those with some experience with SQL and programming and interested in Google Cloud.

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About the Author

Gaurav Sharma

Gaurav started his career as a Corporate and Investment Banking intern at Citi in 2009 and eventually ended up as an Associate Director at Standard Chartered Bank’s Wholesale Banking division a few years later. By 2016, Gaurav was consulting FinTech start-ups in London with product development in the institutional banking space. He also advises mid-market Private Equity/ Asset Management firms and Banks in North America and Europe with investments in the financial services and FinTech sector. Gaurav writes on topics ranging from European Union banking regulations and FinTech to Blockchain startups and the inevitable rise of our AI overlords! He has an Engineering degree in Computer Science and an MBA with a double major in Finance and Marketing. He is also a Certified Financial Risk Manager.