Data Science Skills.pptx

Data Science Skills 1. Programming Programming languages, such as Python or R, are necessary for data scientists to sort, analyze, and manage large amounts of data (commonly referred to as “big data”). As a data scientist just starting out, you should know the basic concepts of data science and beg...

Data Science Skills 1. Programming Programming languages, such as Python or R, are necessary for data scientists to sort, analyze, and manage large amounts of data (commonly referred to as “big data”). As a data scientist just starting out, you should know the basic concepts of data science and begin familiarizing yourself with how to use Python. Popular programming languages include: Python R SAS SQL 2. Statistics and probability In order to write high-quality machine learning models and algorithms, data scientists need to learn statistics and probability. For machine learning, it is essential to use statistical analysis concepts like linear regression. Data scientists need to be able to collect, interpret, organize, and present data, and to fully comprehend concepts like mean, median, mode, variance, and standard deviation. Here are different types of statistical techniques you should know: Probability distributions Over and undersampling Bayesian and frequentist statistics Dimension reduction 3. Data wrangling and database management Data wrangling is the process of cleaning and organizing complex data sets to make them easier to access and analyze. Manipulating the data to categorize it by patterns and trends, and to correct any input data values can be time-consuming but necessary to make data-driven decisions. This is also related to understanding database management—you’re expected to extract data from different  Altair Talend Alteryx Trifacta Tamr And database management tools include: MySQL MongoDB Oracle 4. Machine learning and deep learning As a data scientist, you’ll want to immerse yourself in machine learning and deep learning. Incorporating these techniques helps you improve as a data scientist because you’ll be able to gather and synthesize data more efficiently, while also predicting the outcomes of future data sets. For example, you can forecast how many clients your company will have based on the previous month’s data using linear regression. Later on, you can boost your knowledge to include more sophisticated models like Random Forest. Some machine learning algorithms to know include: Linear regression Logistic regression Naive Bayes Decision tree Random forest algorithm K-nearest neighbor (KNN) K means algorithm 5. Data visualization Not only do you need to know how to analyze, organize, and categorize data, but you’ll also want to build your skills in data visualization. Being able to create charts and graphs is important to being a data scientist. With strong visualization skills, you can present your work to stakeholders so that the data tells a compelling story of the business insights. Familiarity with the following tools should prepare you well: Tableau Microsoft Excel PowerBI 6. Cloud computing As a data scientist, you'll most likely need to use cloud computing tools that help you analyze and visualize data that are stored in cloud platforms. Some certifications will specifically focus on cloud services such as: Amazon Web Service (AWS) Microsoft Azure Google Cloud These tools provide data professionals access to cloud-based databases and frameworks that are key for advancing technology. They are used in many industries now, so it is important in data science to become familiar with the concepts behind cloud computing. 7. Interpersonal skills You’ll want to develop workplace skills such as communication in order to form strong working relationships with your team members and be able to present your findings to stakeholders. Just as data visualization is important for communicating the data insights you uncover as a data scientist, so is being able to collaborate with teams successfully. Here are interpersonal skills you can build upon:  Active listening Effective communication skills Sharing feedback Attention to detail Leadership Empathy Public speaking