Data Science: Skill Guide 2020

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Data Science is a multidisciplinary field and has always been about combining the tools and technologies best suited to get the job done. It is about the extraction of knowledge from data to answer a particular question. Simply putting, data science is a power that allows businesses and stakeholders to make informed decisions and solve problems with data.

Career opportunities in Data Science have exponentially grown in the recent few years. Companies are eager to capture data and derive insights from it because of the technological advancements we are seeing. Accessibility of the data today can help to reap multiple benefits organizations from it. Because of this reason, companies are not shying away from offering increased Data Scientist salaries.

In no particular order, let’s get to know the Top 10 Skills for a Data Scientist in 2020!

1. Probability & Statistics

Data Science is about using capital processes, algorithms, or systems to extract knowledge, insights, and make informed decisions from data. In that case, making inferences, estimating, or predicting form an important part of Data Science.

Probability with the help of statistical methods helps make estimates for further analysis. Statistics is mostly dependent on the theory of probability. Putting it simply, both are intertwined.

What can you do with Probability and Statistics for Data Science?

  • Explore and understand more about the data
  • Identify the underlying relationships or dependencies that may exist between two variables
  • Predict future trend or forecast a drift based on the previous data trends
  • Determine patterns or motive of the data
  • Uncover anomalies in data

Especially for data-driven companies where stakeholders depend on data for decision making and design/evaluation of data models, probability and statistics are integral to Data Science.

2. Multivariate Calculus & Linear Algebra

Most machine learning, invariably data science models, are built with several predictors or unknown variables. A knowledge of multivariate calculus is significant for building a machine learning model. Here are some of the topics of math you can be familiar with to work in Data Science:

  1. Derivatives and gradients
  2. Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function
  3. Cost function (most important)
  4. Plotting of functions
  5. Minimum and Maximum values of a function
  6. Scalar, vector, matrix and tensor functions

Summary

Linear Algebra for Data Science: Matrix algebra and eigenvalues

Calculus for Data Science: Derivatives and gradients

Gradient Descent from Scratch: Implement a neural network from scratch

3. Programming, Packages and Softwares

Data Science essentially is about programming. Programming Skills for Data Science brings together all the fundamental skills needed to transform raw data into actionable insights. While there is no specific rule about the selection of programming language, Python and R are the most favored ones.

In no particular order, here’s a list of programming languages and some packages for Data Science to choose from:

  1. Python
  2. R
  3. SQL
  4. Java
  5. Julia
  6. Scala
  7. MATLAB
  8. TensorFlow

4. Data Wrangling

Often the data a business acquires or receives is not ready for modeling. It is, therefore, imperative to understand and know how to deal with the imperfections in data.

Data Wrangling is the process where you prepare your data for further analysis; transforming and mapping raw data from one form to another to prep up the data for insights. For data wrangling, you basically acquire data, combine relevant fields, and then cleanse the data.

What can you do with Data Wrangling for Data Science?

  1. Reveal a deep-lying intelligence within your data by gathering data from multiple channels
  2. Provide a very accurate representation of actionable data in the hands of business and data analysts in a timely matter
  3. Reduce processing time, response time, and the time spent to collect and organize unruly data before it can be utilized
  4. Enable data scientists to focus more on the analysis of data, rather than the cleaning part
  5. Lead the data-driven decision-making process in a direction supported by accurate data

5. Database Management

Database Management quintessentially consists of a group of programs that can edit, index, and manipulate the database. The DBMS accepts a request made for data from an application and instructs the OS to provide specific required data. In large systems, a DBMS helps users to store and retrieve data at any given point of time.

What can you do with Database Management for Data Science?

  1. Define, retrieve and manage data in a database
  2. Manipulate the data itself, the data format, field names, record structure, and file structure
  3. Defines rules to write, validate and test data
  4. Operate on record-level of database
  5. Support multi-user environment to access and manipulate data in parallel

Some of the popular DBMS include: MySQL, SQL Server, Oracle, IBM DB2, PostgreSQL and NoSQL databases (MongoDB, CouchDB, DynamoDB, HBase, Neo4j, Cassandra, Redis)

6. Data Visualization

Data Visualization is one of the essential skillset for Data scientists as it becomes neccessary for data scientists to study trends in huge volume of data to derive insights which would anyways be problematic for data scientist.

Histograms, Bar charts, Pie charts, Scatter plots, Line plots, Time series, Relationship maps, Heat maps, Geo Maps, 3-D Plots, and a long list of visualizations you can use for your data.

What can you do with Data Visualization for Data Science?

  1. Plot data for powerful insights
  2. Determine relationships between unknown variables
  3. Visualize areas that need attention or improvement
  4. Identify factors that influence customer behavior
  5. Understand which products to place where
  6. Display trends from news, connections, websites, social media
  7. Visualize volume of information
  8. Client reporting, employee performance, quarter sales mapping
  9. Devise marketing strategy targeted to user segments

Some of the popular Data Visualization tools include: Tableau, PowerBI, QlikView, Google Analytics (For Web), MS Excel, Plotly, Fusion Charts, SAS

7. Machine Learning / Deep Learning

Machine Learning for Data Science includes algorithms that are central to ML; K-nearest neighbors, Random Forests, Naive Bayes, Regression Models. PyTorch, TensorFlow, Keras also find its usability in Machine Learning for Data Science

What can you do with Machine Learning for Data Science?

  1. Fraud and Risk Detection and Management
  2. Healthcare (one of the booming Data Science fields! Genetics, Genomics, Image analysis)
  3. Airline route planning
  4. Automatic Spam Filtering
  5. Facial and Voice Recognition Systems
  6. Improved Interactive Voice Response (IVR)
  7. Comprehensive language and document recognition and translation
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Conclusion

As Data Science is a complex field and involves a lot of intermixing of different tools and technologies to solve a particluar problem.

Tools and technologies listed above finds the foundation of Data Science on which the complex problems are being solved.

Comments

Nice blogs . Thanks a lot

- Nirav

very helpful and updated to latest technology. Thanks for helping us!!

- Yogesh

Nice Blog...

- Sandip

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