Introduction
With the drumbeat for big data across the industries getting louder due to the COVID-19 crisis, aspiring data professionals must come to terms with working in the data realm.
Despite people losing jobs during the pandemic, the data science industry still managed to pull through the year successfully as compared to the other industries. The industry had lesser pay cuts, furloughs, and fewer layoffs. One of the major reasons is because most professionals had access to the tools and technologies despite work from home implementation.
As data keeps generating in massive amounts, it is clearly evident that a data science career is a great option to keep your future secure. A report by Analytics Insight states the global big data market is estimated to rise to USD 301.5 billion by 2023 from USD 179.6 billion in 2019.
Taking into consideration, becoming a data science professional can help transform any organization. One of the major reasons why organizations are looking to hire expert professionals who can help their business grow.
Without wasting much time, we will talk about the skills needed to become a data scientist,
To deploy data science, you need to understand how software development works and the tools included. For better understanding, we have categorized the below top 10 emerging
skills in data science. We’re all aware of the foundation needed to learn data science (programming skills, business acumen, mathematics, and statistics). However, there’s more than just basic skills. Here’s what you need to grasp knowledge in today,
Docker
If you’re looking to dig deeper into the data development world, data engineering, and DevOps then learning Docker should be your goal. Around 44 percent of developers are interested in adding Docker to their skillset.
Natural Language Processing (NLP)
NLP continues to remain a critical skillset amongst data professionals. Gaining in-depth knowledge in NLP is an added advantage for professionals seeking a data science career.
Deep Learning Algorithms
How do you think self-driving cars work? Well, deep learning a subset of machine learning is touted to be a desirable skill set. Nearly 50.8 percent of respondents who had taken a survey commissioned by KDnuggets said that deep learning is not a fad.
Reinforcement Learning
Reinforcement learning was positioned as the topmost skill every data science professional needs to add to their portfolio. Around 51.9 percent of the respondents agreed to the fact.
PyTorch
Nearly 50.1 percent of the respondents said they would be interested in adding this machine learning library to their skill set. PyTorch is an ideal solution that is used for projects related to natural language processing and computer vision.
TensorFlow
Around 51.2 percent of respondents who took the survey said they all need to improve their skills in TensorFlow or perhaps add it to their list of learning skills. With TensorFlow gaining interest in the industry, it remains to be one of the most critical
data scientist skills.
Amazon Web Services (AWS)
Where would we go, and save our data if not for services provided by cloud computing? Acquiring AWS skills spiked the interest of 48.8 percent of the respondents. While Amazon offers a wide range of services learning AWS could be of great value.
Apache Spark
Apache Spark is a framework that helps process data. It is fast, scalable, and developer-friendly. Besides the distribution of data on multiple computers, it can also perform quick data processing tasks on large data sets. Near about 45.3 of respondents showed interest in learning more about Apache Spark.
Computer Vision
If you’re looking to find one of the most powerful forms of AI, then you’ve got the example right here. Computer vision is the perfect example of AI – it is a field that replicates parts of the complexity of the human visual system and further enables the computer system to process objects and images in the same manner as humans do.
NoSQL Databases
NoSQL databases help store or retrieve data without needing to define its structure first. You can now use NoSQL databases instead of relational databases wherein data can be placed in the form of tables. Some of the commonly used NoSQL databases include names like Hadoop, HBase, Hypertable, Cassandra, Apache Accumulo, Flink, and Splice.