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Every job sector is transforming in this technology driven economy and Data Science is no exception. For those in data science, we have some good news: Demand for data scientists is fast growing and 2020 is expected to become the year of innovative technologies in this field.

The year 2019, saw the demand for data scientists rise exponentially compared to other technology professionals. As these figures suggest, being a data scientist is the coolest gig out there and skill development is the best option for every professional.

Reskilling and upskilling is critical for those in the data science field to stay ahead of the game and become competitive. At the same time, adequate preparation and learning developer tools is vital for data scientists as the industry evolves. As a principal data scientist at Galvanize and with industry experience, I would say this: Adopt diverse working approaches and learn the right tools to prepare for changes in data science as we head into the future.

Before we begin, think about this question: As a data scientist, what are my current skill sets, and what areas need my attention in 2020 to become better? Let’s review some hands-on skills you need to succeed as a data scientist in 2020

1. Machine Learning and Deep Learning

Here’s the reality: As a data scientist, you need extensive knowledge on core skill areas including #machinelearning and #deeplearning. From PyTorch to TensorFlow and Keras; you need these tools to become a top-notch data scientist as recommended by industry experts. We should also expect new ML and DL releases in 2020 that will dominate training programs as well. PyTorch became widespread in 2019 and analysts predict more advancements with TensorFlow 2.0 release, which is considered a must-have tool for every data scientist. The bottom line is that you need a hands-on understanding of these tools/releases in order to hit the ground running in 2020.

2. Natural Language Processing

Natural language processing has evolved with new technologies helping companies to derive value from this innovation. Advancements including transformers and bi-directional sequencing are some of the latest models used by data scientists because of their cost effectiveness for NLP models. BERT is another example of pre-trained models used and accordingly saves time for data scientists, which makes it a top priority for 2020.

Other examples of pre-trained NLP models data scientists should consider include GPT-2 and Elmo commonly used in the industry and offer impressive results. Corporate organizations are turning to NLP for customer service needs to raise their competitive ability while assisting them to forecast market trends. Investments in voice technology are also accelerating according to IHS Markit and with new NLP service providers cropping up; we can evidently see the need for data scientists to train in NLP.

3. Statistics

Organizations benefit from statistical analysis as data scientists tinker with them to map patterns and generate results for strategic management purposes. Data driven companies find value in statistics given the enormous information mined from data sets. Because of stiff competition experienced in this age of technology, organizations have no choice but to understand and adopt statistical methods in their operations.

Analysis of information and trends by companies depends on statistics and goes to show the importance of this skill for every data scientist. Sometimes, it gets difficult decoding data patterns and through statistical knowledge, a data scientist can detect errors and correct them by testing algorithms.

4. Programming Skills

Asa data scientist, you need to understand your expectations each day such as procuring, cleaning and data organization, all which require programming languages e.g. Python and R. There are many programming languages currently used in the industry and depends on the interest areas of the data scientist.

However, for the most part, Python and R command a 50% usage rate according to ZDnet and so every data scientist should take note of these two languages. You can also choose from a wide range of other languages including Java, SQL and MATLAB but as I have mentioned, it all depends on your current focus and the direction you are taking.

5. MLOps & Workflow

When deploying data science projects in the production phase, MLOps stands out as the best alongside engineering of data. Tools for machine learning such as AutoML increased their popularity in 2019 as users adopted them because of productivity during cycles. The same applies to Airflow, Kubeflow and MLFlow, all of which are being adopted in the mainstream. A data scientist needs these tools for labeling, testing and deployment reasons, with analysts forecasting the demand to grow in 2020.

Given that modeling costs keep reducing each year, we can observe that adoption of these tools will continue because of fast deployment during production phases. You will require knowledge of these tools in 2020 because of handling tasks such as building, testing, deployment and monitoring models.

6. Git and Agile

Doyou want an easier way for managing software versions? Then Git is the best choice considering its ability to map adjustments implemented in code and furthermore, the simplicity of coordinating with different developers undertaking the same project. GitHub is among the best-preferred platforms for data scientists as the era of developer tools kicks in, and today alternative such as Gitlab are growing in popularity. Every data scientist works with developer tools and Git is an essential part of the workflow.

On the other hand, agile work systems include work arrangements commonly used by developer teams, and given that every data scientist is involved with software development, this has made the work of #machinelearning experts easier and productive. ML engineers and data scientists work as developers and through agile workflows and sprints, they can refine their codebase elements in the best way possible. Accordingly, a data scientist should have an understanding of agile work patterns in accordance with the scrum approach to achieve optimal results.

7. Big Data and Cloud

Big data is everywhere, and every data scientist needs knowledge in cleaning, sorting and managing structured and unstructured sets of data. Additionally, data scientists rely on programming and data wrangling skills to handle vast data samples. To this end, data scientists use these big data methods to study and unearth information needed for decision-making in corporate organizations. It is a requirement that data scientists need a good understanding of big data to enable them to retrieve information, oversee its management and analyze for context.

Most often, Hadoop and Spark are commonly used in data preparation, distributing and processing data as well. Just to mention, Hadoop continues to be popular among data scientists, both pre and post Cloudera acquisition, for its ability to process data in real time. Lastly, you need to acquaint yourself with data roles such as exploration, filtering and sampling.

We cannot exhaust our list of skills without mentioning cloud technology that makes it easy for machine learning engineers to develop an ideal environment for their workflows. For example, using platforms including Google Cloud, Microsoft Azure and AWS is critical for #machinelearning experts because of easy access to work remotely. For this reason, data scientists should have knowledge about the functionality of cloud technology in order to maximize their work output.

8. Data Visualization and Data Wrangling

Communication is critical for any data scientist looking forward to explaining findings to the audience and this is what data visualization entails. For instance, the data scientist has to understand visualization coding followed by transmission of information since they matter to audiences with technical backgrounds and those who are non-technical. Acquiring these skills is important for the data scientist to make sense of their work and ensure the audience understands how to apply them in their contexts.

Alternatively, data wrangling revolves around detecting and addressing existing errors in data sets where the data scientist should get ahead by resolving internal errors that often disrupt accuracy of the model. For instance, corrupted information prevails in most situations and without addressing these loopholes, the results will not meet accuracy thresholds.

This is where data wrangling comes in and as 2020 begins, you should be aware of this skill to make your work tick. Additionally, wrangling data skill ensures that the data scientist sorts information accurately leading to better output. The difference between a skilled and unskilled data scientist comes down to data processing and applying data in analytics.

As we Wrap things up

The year 2020 is set to be a defining year for the data science field and with new tools coming out; it is evident that you cannot afford to be comfortable with your current skill sets.

Yes, I understand that Python, R and SQL are important programming languages for data scientists, but with the current industry transformation, developing these skills is essential. As #artificialintelligence and the #futureofwork keep progressing, we can only imagine the benefits awaiting data scientists if they acquire the skills we have discussed. Are you ready for 2020 as a data scientist? Everything comes down to learning new developer tools and applying them in your work environment.

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