You intend to work as a data scientist, then? Aspiring data scientists, take note! It's a terrific moment to be a data scientist, with a thriving employment market, high incomes, and exciting career prospects. But what if you're not sure where to begin? Fortunately, there are a wide range of educational options available to data scientists. Obtaining a formal certification or qualification is typically necessary to work as a data scientist, but there are many other ways to learn the skills needed in the field: from earning a computer science degree in college to attending bootcamps that teach programming languages, data visualisation, and machine learning models to teaching yourself computer science fundamentals and analysis.
Thus, acquiring knowledge in data science doesn't require a full-time career. By taking a less common route, many data scientists, engineers, analysts, and other professionals have excelled in their fields. There are other paths to becoming a data scientist and making more money than the typical data engineer or scientist, aside from obtaining a bachelor's degree in data analysis.
Not sure where to begin? We'll walk you through the process of becoming job-ready in the field of data science and in your new career in this article!
Why Data Science?
Businesses now recognise the value of data, which has propelled data science to the forefront of the software sector. Today's expanding companies require someone with the appropriate data science abilities, like you, to source and handle data efficiently. Businesses use data scientists, data analysts, and other data specialists to produce insights that enable them to outsmart rivals and increase revenue.
As a result, there are many chances in the field of data science. According to BLS projections, there will be about 30% growth in this profession by 2026. This explains in part why "Data Scientist" is one of the top three technology careers according to US News. Gaining knowledge in data science can pay dividends soon.
Becoming a data scientist is worthwhile, but learning the field and finishing a project won't be simple.
Salary increases reflect the competition amongst organisations to attract top people. According to data from the University of San Francisco, the median pay for graduates of its MS in Data Science programme is $125,000. After completing the programme, more than 90% of graduates obtained full-time jobs within three months; as a data scientist, employment is all but guaranteed.
You might be wondering, before you get right into the field of data science, just what a data scientist does. Let's investigate.
What does a Data Scientist do?
A data scientist transforms information into insightful knowledge. Upper management bases its business judgements on these insights. It's impossible to predict what a data science career will entail or where it might lead as data scientists work in a variety of capacities and responsibilities.
Data will be gathered, cleaned, and analysed by a data scientist. Data in its unstructured state is too difficult to analyse, thus cleaning is always required. Usually, there are corrupted volumes, missing entries, etc. To clean that data, data scientists employ statistical techniques and engineering knowledge.
Next, the data scientist will search for patterns in the data using an exploratory data analysis. Writing algorithms and building machine learning models that can be used to conduct experiments on datasets and get valuable insights is how data scientists accomplish this.
Data scientists then share their discoveries with management and other teams. Presentation and data visualisation abilities are frequently needed for this.
As a data scientist, you will most likely:
Determine the situations in which data can be applied to address issues.
sources of information that are useful in resolving the issue.
Make sure the data is accurate and up to the organization's requirements.
To get insights, create models and use algorithmic techniques.
Tell stories and visualise data to present findings to different stakeholders.
Now that we are aware of the duties of a data scientist, let's examine how to become one if you are new to the profession.
Steps to Become a Data Scientist
To become a data scientist, you must become proficient in a variety of machine learning technologies, computer languages, and data science ideas. The steps for learning data science from scratch are as follows.
Build a Strong Foundation in Statistics and Maths
Math is fundamental to working in data science and will provide you with a solid theoretical basis in the field, just like many other science disciplines. These abilities are necessary for data scientists to finish their work.
The most crucial concepts to understand while working in data science are probability and statistics. The majority of the models and algorithms developed by data scientists are essentially programmed adaptations of techniques used in statistical problem solving.
You can begin with a 101 course if you're new to probability and statistics. Take use of this to learn about variance, correlations, conditional probabilities, and the Bayes theorem—basic topics. By doing this, you'll be in an excellent position to comprehend how those ideas apply to the work you'll be doing as a data scientist.
Recall that it's simple to become overwhelmed when learning data science; just keep going! Learning data wrangling, getting the hang of organising data, mastering basic ideas like predictive modelling, learning a programming language, gaining practical experience with various tools and data sets, extracting actionable insights from information, and finishing real-world data analytics projects are all necessary to become a data scientist. In the field, effective communication skills are just as crucial as technical expertise. The essential talents are more valuable to potential employers than anything else, not even a bachelor's degree.
Get Familiar with Data Bases
To get the data they are working with and store it after processing, data scientists must be proficient with databases. You will require these abilities if you wish to work as a data scientist!
One of the most used database query languages is called Structured Query Language (SQL). It lets you make tables and views, edit records, and store new data. A further benefit of big data systems like Hadoop is that they include extensions that let you do SQL queries.
You don't need to have a thorough understanding of database technologies to become a data scientist. Let the database admins handle that. To be a data scientist, all you have to do is learn the exact query procedures that are needed to get and store data from relational databases.
Learn Analysis Methods
A dataset can be analysed by data scientists using a variety of techniques. The particular strategy you choose will depend on the kind of data you're utilising and the problem you're trying to answer. It is your responsibility as a data scientist to possess the foresight necessary to determine which approach will be most effective for a given situation.
In the sector, a few analysis methods are frequently employed. Regression, time series, cohort, and cluster analysis are all included in this.
It's not necessary for a data scientist to be familiar with every data analysis technique available. It's more crucial that you comprehend when to apply a specific strategy. Those who can swiftly match problems with data analysis approaches are the greatest data analysts.
Can You Learn Data Science on Your Own?
With the help of YouTube videos or online courses, you can learn data science independently. If you want to pursue a career in this area, there is no shortage of educational resources available on the Internet. But it is always better to opt for a data science certification course in Delhi, Mumbai, Kolkata, Gurgaon or in any other city of India to be more professionally equipped.
Nevertheless, self-learning is unstructured, and you might not be aware of the crucial components you're overlooking. For individuals seeking both freedom and support, data science courses and bootcamps offer a satisfying balance because they offer a supportive cohort environment and an experienced instructor to provide feedback.