Data Science School

So you’re considering a career in data science. This challenging and equally rewarding and high-paying career path offers seemingly limitless opportunities in the current job market. But you’re probably wondering what is the best way to break into the field? Is there such a thing as a data science school?

There are a number of learning options available to aspiring data science students. Data science combines multiple fields like programming, visualization, software development, statistics, machine learning, big data and artificial intelligence. Each of these is an independent vertical, so trying to be an expert in all is not advised. But having an understanding of each of them will help you decide what to study.

We took a broad look at the opportunities out there for  aspiring data scientists, along with an overview of the key data science skills and concepts you’ll need to master on your mission to becoming a data expert. We’ll focus on the online learning route, since this currently offers the most efficient and cost-effective pathway to becoming a data scientist.

Demand for Data Scientists

Almost every business these days is taking advantage of the awesome power of data. This is achieved through the use of scientific methods to extract and analyze the data, developing insights that can help drive a business towards its goals. Insights are derived using a complex set of algorithms and tools.

The increasing application of data science in business has had an impact on all industries. This has translated into an increasing demand for data scientists across various sectors. And the ugly truth is, although the demand is there, the workforce is severely lacking in professionals with the skills or experience to do the job. According to the LinkedIn Workforce Report, there were more than 151,000 data scientist jobs lying vacant across the US in 2018. For those with the right qualifications and skills, the field of data science is a goldmine.

Data Science Opportunities

Earning a qualification in data science will give you the capabilities to apply with top tech firms. As we’ve already mentioned, the field is wide and there are huge opportunities in data science. Once you understand the job requirements for the companies in your sights, work on developing essential proficiencies. Some of the job titles offered in the field of data science include:

• Data Analyst
• Business Analyst
• Data Scientist
• Machine Learning Engineer
• Big Data Analyst
• Data Miner
• Business Analyst

There’s significant overlap among different job descriptions. But each role starts with a strong grasp of the basics. So let’s take a look at the key concepts and skills you’ll learn through an online data science school.

Learn Data Science Online

Our data science school comes in the form of both a full-time data science course and a part-time, flexible format. Each course is built to help launch a career in data science.

Taught by experts in the field, our programs also include one-on-one mentorship to help you learn the curriculum and tackle real-world projects. Following the coursework, you get six months of dedicated career coaching to guide you into your new career. You'll also get a data science certificate to show off your expertise.

Both options are beginner-friendly courses that offer a birds-eye-view of what the field is all about. The courses are an introduction to data science and cover all the key concepts like analysis, experimentation, machine learning and specializations. Let’s take a look at each of those areas in a little more detail.

SQL:

A data scientist deals with large chunks of data. This involves fetching relevant data from a universal server to work on it. This can be done with the help of a programming language called SQL (Structured Query Language).

Many online data science programs offered by other schools don’t teach SQL, as it’s considered basic. But a beginner with no computer science background will find it difficult to understand data science without learning SQL. So our mentors emphasize the value in learning these sorts of basics and getting the foundation right.

Python:

Python is a programming language used to get insights from data. Touted to be one of the easiest programming languages to learn, the algorithms are relatively simple in structure. Visualization is a great way to produce insights, and Python is a great tool for producing them.

Real time experimentation:

What’s the use of learning without applying it in real time? Hands-on experience helps you better understand the principles. After completing this module, you’ll be assigned data sets to test. You’ll be testing data in real-time and creating visualization patterns from it. This process helps in deriving insights from the data set.

Machine learning

Machine learning (ML) is a major component of data science. We all know that programming helps a machine perform its functions. If the function changes, the programming needs to be subsequently changed. With every change in function, a human-machine interaction takes place. ML essentially focuses on reducing this interaction. With the help of artificial intelligence (AI), machines become equipped to learn from experiences. Repetitive tasks can be automated using this technique, resulting in drastically reduced lead times.  

As you study, you’ll learn two subsets of machine learning: supervised learning and unsupervised learning.

Supervised learning:

Scikit-learn is a python library used to build mathematical models for machine learning. It allows you to govern a set of inputs and desired outputs. ML has few classical models that help in governing desired outputs. Decision trees (which are a supervised ML model) and Ordinary Least Squares (OLS) are widely used. These learning tools will then be incorporated in a real time project to give you hands-on experience.

Unsupervised learning:

In the case of unsupervised learning, outcomes won’t be fixed. Instead, you’ll focus on deriving relationships among the metrics present in data. Working with large data is possible. But will it help in understanding insights better? Probably not. It’s more useful to break the main data into smaller chunks. Algorithms like ‘k-mean’ do that.

Specializations

Data science is a diverse field. It consists of many subsets, each with a different area of application. It’s virtually impossible to learn all the concepts of data science. That’s why we offer three different specializations for learners:

1. Advanced Natural Language Processing
2. Deep Learning with TensorFlow and Keras
3. Big Data and Spark

Each specialization is completely independent, and needs to be understood in detail to excel.

Thinkful Support

At Thinkful, we make sure our deliverables are in line with our students’ expectations. That’s why we’re one of the fastest growing online bootcamps. Our admissions process is second to none, and the support we offer includes:

Application and fit interview:

Looking for courses online can be overwhelming. It can be hard to understand what each curriculum is offering. Once you complete the application form for Thinkful, one of our admissions reps will get in touch with you to explain the program structure, modules, assignments, and career path offered by the program. You can ensure you have all the relevant information about the course before you commit.

Industry acceptance:

Our thoughtfully curated curriculum is highly regarded by tech industry recruiters. Most of our students get a job within 180 days of graduation. Our alumni work in big tech companies like Google, Amazon and IBM. Having former students like this in your network could help you land a position in your dream company. Come with an open mind, learn and get hired.

Learn from experts:

Data science as a field is vulnerable to sudden changes. So, it’s essential to ensure you’re learning the most up-to-date industry practices. Our talent pool of passionate, energetic, and brilliant mentors have an average of 10 years’ teaching experience. Learn from them and be ready to enter the world’s next workforce.

Trial interviews:

Acing a job interview takes a lot of practice. Keeping calm is the secret to performing well. But this doesn’t come naturally and requires regular practice. To help you achieve this, we conduct five trial interviews throughout the course. The panel will include hiring managers from corporates to gauge your skills and problem-solving abilities. After the interview, feedback will be given to help you improve.

Job coach:

There are a lot of nuances to understand before applying for a tech role. To help you with this process, we assign a job coach to all students. After graduating, a personal coach will guide you in applying for top companies. They’ll stick with you for six months, after which you can waive the cost of tuition if you haven’t landed a great job.

Learn Data Science with Thinkful

Data science is a vast field and your subject knowledge should be detailed and ready to adapt to changes in technology. The industry is prone to rapid change, so learning different tools and mastering them is vital. Read more about data science for beginners before you take the plunge, or check out the experiences of a Thinkful grad for further inspiration.