Data science is a niche in the large global IT market which is expected to drive trillions in spending in 2020 and beyond. With the COVID-19 outbreak, the priority of companies shifted slightly from data science, but there was a large increase in data science job listings from August 2020. This means that the industry grew despite the effects of the pandemic on the global economy. Companies were able to increase data operations to include artificial intelligence capabilities and incorporate them into business processes even though they are non-tech. This singlehandedly increased the demand for data scientists. If you are considering a career in data science, here are some skills needed to start:
1. Programming
Knowledge of programming languages is essential as a data scientist. The R programming language is preferred in most cases because it is designed for data scientists. R can be used to solve different problems in data science. More than 43% of data scientists use R to solve statistical problems. The only challenge is that it may be difficult to learn if you haven’t mastered a programming language already. However, other resources can help such as Bootcamprankings.com.
2. Python Coding
In most data science job roles, Python is commonly required along with Perl, Java, and C/C++. It is an amazing programming language for data science. Around 40% of data scientists use it as it is quite versatile. It can be used for all steps in data science processes such as taking formats of data and importing SQL tables into the code. Datasets can be created with Python as well.
3. SQL Database/Coding
Hadoop and NoSQL are large parts of data science but experts still need to be able to use SQL to write and execute tough queries. Structured query language allows users to carry out operations such as delete, add, and extract data from the database. It is also used to transform database structures and handle analytical structures. As a data scientist, you must be proficient in SQL. It is designed to help data scientists access data, communicate, and work on the same data. It offers insights when used to query a database. The language also has concise commands that save time and reduce the amount of programming needed for difficult queries. SQL helps you understand relational databases better, and it also boosts your profile.
4. Apache Spark
This computation framework is just like Hadoop, but the difference is that Apache Spark is faster. It caches its computations while Hadoop reads and writes making it slower. Spark is designed for data science as it helps run the algorithm faster. Also, it disseminates the data process to reduce the time needed when the data is large. Data scientists can use it to handle complex unstructured data and, on a machine, or a cluster of machines, it also prevents the loss of data. Its speed makes it stand out.
5. AI and Machine Learning
So many data scientists lack machine learning skills like neural networks, adversarial learning, and reinforcement learning. To stand out, you need these machine learning techniques like decision trees, supervised machine learning, and logistic regression. The skills can help you to solve data science problems based on predictions of organizational outcomes. You may also need to apply your skills in other areas of machine learning such as recommendation engines, natural language processing, time series, and unsupervised machine learning. As a data scientist, you will work with large data sets. In this case, machine learning will come in handy.
6. Data Visualization
Data is king as a popular adage goes. Businesses produce a lot of data frequently. This data must be translated so others can comprehend it easily. It is easier to put it in graphs and charts. As a data scientist, you should visualize data using different tools like ggplot, d3.js, Tableau, and Matplotlib. The tools allow you to convert the results to a format that others can easily comprehend. Data visualization allows organizations to use data to act on new opportunities in business so they can stay ahead of their competition.
Conclusion
Data science has grown in popularity since 2016, and even though the industry dropped slightly in priority, it still performed better than many other industries. As the pandemic worsened, more non-tech companies had to increase data operations to make their decision-making processes easier. The demand for data scientists rose again and it is predicted that it will continue to grow. If you are considering a career in data science, then you need to master programming languages like Python, R, SQL, and more.