Must-Have Data Science Skills - Part4

Data Science, AI and ML
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Let us talk about some of the soft skills to become a successful data scientist

Data Science Skill #1: Communication Skills

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“Good communication is just as stimulating as black coffee, and just as hard to sleep after.” – Anne Morrow Lindbergh

Data Science projects are more of a treasure hunting job, the treasure being the insights you fetch from the data. The question is what is the price of the treasure? Well, that is decided by your stakeholders. The only way to get a good price is to be able to communicate how insightful the results and how can this treasure help them in improving the profits and organization.

Furthermore, the quality of a great data scientist is to formulate the problem statement. At the start of the project, the stakeholders tell their requirements to the data scientist, and then the latter formulate a problem statement. For example, the stakeholder needs to improve the content recommendation of their OTT platform so that the retention time increases. This is a very vague description, it’s the job of the data scientist to communicate the right problem statement.

Data Science Skill #2: Storytelling Skills

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Imagine watching a cricket match stats, you are shown with the runs scored on each bowl in the form of a table. Do you think you will get any important information from this? What if you are you are shown a bar chart of runs scored in each over? Seems better. Right? It is not in human nature to understand blocks unless you make them interactive.

Storytelling is the utmost important acquired skill by a data scientist.

Data Science Skill #3: Structured Thinking

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Let us say that you want to become a data scientist – you will break this large goal into multiple parts like training, preparing your resume, applying for a job likewise the ability to break down a problem into multiple parts so as to efficiently solve it is Structured thinking.

A Data Scientist always looks at problems from different perspectives. This is an acquired skill but you can definitely work on it.

Data Science Skill #14: Curiosity

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Why did this happen? How did this happen? If I tweak this, will it affect the overall results? Continuously asking questions is one of the most crucial soft skills of a data scientist. If you are dull, you may follow all the steps of the machine learning project lifecycle but you won’t be able to reach the end goal and justify your result.

Data Science is still evolving and it let me tell you the most important thing – Learning never stops in this field. You master the tool one day and it gets run over by an advanced tool the next day. A data scientist needs to be curious and always learning.

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Continuing with our discussion from the last post, below is the continued roadmap of the journey.

  1. Machine Learning: In the not-too-distant future, process automation will replace most human labour in manufacturing. Devices must be intelligent to match human skills, and Machine Learning is at the heart of AI. For accurate forecasts and estimations, Data Scientists must grasp Machine Learning. This can assist machines in making better decisions and taking wiser actions in real-time without human intervention. Data mining and interpretation are being transformed by machine learning. Traditional statistical procedures have been superseded by more accurate automatic sets of generic algorithms.
  • How models Work
  • Basic Data Exloration
  • First ML Model
  • Handling Missing Values
  • Cross Validation using R
  • Data Leakage
  1. Deep Learning: Deep learning is a machine learning and artificial intelligence (AI) technique that mimics how humans acquire knowledge. Data science, which covers statistics and predictive modeling, incorporates deep learning as a critical component. Deep learning is beneficial for data scientists responsible for gathering, analyzing, and interpreting massive amounts of data; it speeds up and simplifies the process. Deep learning can be regarded of as a means to automate predictive analytics at its most basic level. Deep learning algorithms are built in a hierarchy of increasing complexity and abstraction, unlike typical machine learning algorithms, which are linear.
  • Artificial Neural Network
  • Convolutional Neural Network
  • TensorFlow
  • Keras
  • A Single Neuron
  • Overfitting and Underfitting
  • Dropout Batch Normalization
  • Binary Classification

7.Feature Engineering: The preparation procedures that transform raw data into features that may be used in machine learning algorithms, such as predictive models, are referred to as the feature engineering pipeline. Predictive models have an outcome variable and predictor variables, and the most effective predictor variables are produced and selected for the predictive model throughout the feature engineering process.

  • Baseline Model
  • Categorical Encodings
  • Feature Generations
  • Feature Selection
  1. Natural language processing: It is an artificial intelligence area that aids computers in comprehending, interpreting, and manipulating human language. In order to bridge the gap between human communication and machine understanding, NLP draws on a variety of fields, including computer science and computational linguistics.
  2. Data Visualization Tools: In today’s data-driven environment, data visualisation is one of the most in-demand skills. As more businesses turn to their data to uncover opportunities, the demand for Data Analysts who can tell their data’s storey is growing. Data visualisation is the act of transforming any raw data, regardless of industry, into a representation of graphs and shapes in order to better understand your data, gain insights, and make better business decisions.
  • Tableau
  • Microsoft Power BI
  • QlikView & QlikSense
  • Excel

10.Deployment: The deployment is the final step. Deployment is absolutely required, whether you are a new hire, have 5+ years of experience, or have 10+ years of experience. Because deployment will undoubtedly show you how hard you worked.

  • Microsoft Azure
  • Heroku
  • Google Cloud Platform
  • Flask
  • Django