Must Have Data Science Skills
No matter what present skills or previous experience you have, there is always a path for you to have a career in data science. What’s important to know is the skills you should be developing and where to learn those skills from. There are some core data science competencies you should develop. A good place to start can be a data science bootcamp program.
Statistics and Probability
In data science, you are required to use algorithms, systems, or capital processes for gathering insights, knowledge, and making informed decisions from the data. That is why an important part of your job would be to make inferences, estimates, and predictions. With the help of probability and statistical methods, estimates can be made for further analysis. They can be used for:
Exploring and understanding more about data
• Identifying the underlying dependencies or relationships that could exist between two variables
• Forecasting a drift or predicting future data trend depending on previous trends
• Determining motive or patterns of data
• Uncovering data anomalies
Statistics and probably are particularly important to data science in data-driven companies where stakeholders are dependent on data to make decisions and evaluate or design data models.
Multivariate Calculus and Linear Algebra
Most data science and machine learning models are built with many unknown variables or predictors. To build a machine learning model, it is essential to have an understanding of multivariate calculus. If you want to work with data science, some mathematical topics you should be familiar with include:
• Gradients and derivatives
• Cost function
• Sigmoid function, step function, Rectified Linear Unit function, and Logit function
• Maximum and minimum values of a function
• Plotting of functions
• Vector, scalar, tensor, and matrix functions
Software, Packages, and Programming
Programming is essential for data science. With programming skills in data science, you can make use of all the required fundamental skills for obtaining actionable insights for raw data. When it comes to selecting a specific programming language, there is no particular rule. However, R and Python are most preferable in data science. Programming platforms or preferences can vary a lot. Mostly, programming languages chosen by data scientists depend on the problem statement. However, Python is a language that seems to apply to most problems. Given below are some packages and programming languages used in data science:
Coding makes up a huge part of data science and it can be difficult if you have no familiarity or experience with coding. Before building up any code, it is advisable to brush up on the programming language you will use.
Usually, the data received or acquired by a business isn’t ready for modeling. Therefore, understanding the data imperfections and knowing how to deal with them is imperative. The process of data wrangling involves preparing data for further analysis and mapping and transforming raw data into one form from another for preparing data for insights. It requires acquiring data, combining relevant fields, and cleansing the data. Data wrangling is used for:
• Revealing deep-rooted intelligence within the data by collecting it from multiple channels
• Providing an accurate representation of data that is actionable
• Reducing the time required to process, respond, organize and collect data before its utilization
• Allowing data scientists to pay more attention to data analysis rather than data cleaning
• Leading a decision-making process that is driven by data
You have to be a jack of all trades if you want to be a data scientist. To become a well-rounded data scientist, you will be required to know mathematics, statistics, visualization, data management, and programming.
A vast majority of the work a data scientist does goes into the preparation of data to process it in an industry setting. Data scientists should know how to manage the data given the huge data chunks they need to work on.
Database management is about using certain programs for editing, manipulating, and indexing the database. The application requests for data and the DBMS accepts it and instructs the operating system to provide the data. With the help of DBMS, storing, and retrieving data in large systems is possible at any point in time. Database management can be used for:
• Defining, retrieving, and managing data in a database
• Manipulating the data format, record structure, field names, file structure, and the data itself
• Defining rules for writing, validating, and testing data
• Operating on database records
• Supporting a multiple user environment for parallelly accessing and manipulating data
Some popular DBMS are SQL Server, MySQL, Oracle, PostgreSQL, IBM DB2, and NoSQL databases like MongoDB, DynamoDB, CouchDB, Hbase, Redis, Cassandra and Neo4j
Data visualization is all about graphically representing the data findings. It is one of the most essential data science skills as it is not all about representation of the final results, but also learning and understanding the data as well as its vulnerability. With data visualization, real value can be understood and well-established. It helps to get information that is meaningful and can potentially influence the system.
Bar chart, histogram, pie chart, line plotting, scatter plotting, time series, heat map, a relationship map, 3D plotting, and Geo map can be used for data visualization. Data visualization can be used for:
• Plotting data to gain important insights
• Determining relationships between variables
• Visualizing areas of improvement and the ones that require attention
• Identifying factors that influence the behavior of customers
• Understanding where to place which products
• Displaying trends from connections, news, social media, and websites
• Devising marketing strategies to target user segments
• Visualizing volume of information
• Employee performance, client reporting, and quarter sales mapping
• Tableau, Google Analytics, PoweBI, MS Excel, QlikView, Plotly, SAS, and Fusion Charts are some of the popular tools for data visualization.
Machine learning is an essential skilll in companies that operate on and manage vast data amounts and where there is a data-centric decision-making process. Machine learning includes algorithms like Random Forests, K-nearest neighbor, PyTorch, Keras, TensorFlow, regression Models, and Naïve Bayes.
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