DATA SCIENTIST EXCEL/PYTHON

DATA SCIENTIST

What does a data scientist?
A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.

1. Sharpen relevant skills.
If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant bootcamp. Here are some of the skills you’ll want to have under your belt.

Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include:
Python

Data visualization: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work:
Excel

Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.

Big data: Some employers may want to see that you have some familiarity grappling with big data. Some of the software frameworks used to process big data include Hadoop and Apache Spark.

Communication: The most brilliant data scientists won’t be able to affect any change if they aren’t able to communicate their findings well. The ability to share ideas and results verbally and in written language is an often-sought skill in data scientists.

Data scientists often deal with the unknown by using more advanced data techniques to make predictions about the future. They might automate their own machine learning algorithms or design predictive modeling processes that can handle both structured and unstructured data. This role is generally considered a more advanced version of a data analyst. Some day-to-day tasks might include:

  • Gathering, cleaning, and processing raw data
  • Designing predictive models and machine learning algorithms to mine big data sets
  • Developing tools and processes to monitor and analyze data accuracy
  • Building data visualization tools, dashboards, and reports
  • Writing programs to automate data collection and processing

DATA ANALYSIS  typically work with structured data to solve tangible business problems using tools like SQL, R or Python programming languages, data visualization software, and statistical analysis. Common tasks for a data analyst might include:

  • Collaborating with organizational leaders to identify informational needs
  • Acquiring data from primary and secondary sources
  • Cleaning and reorganizing data for analysis
  • Analyzing data sets to spot trends and patterns that can be translated into actionable insights
  • Presenting findings in an easy-to-understand way to inform data-driven decisions

Difference between Data Scientist and Data Analysts

Data skills for scientists and analysts. Data scientists and data analysts both work with data, but each role uses a slightly different set of skills and tools. Many skills involved in data science build off of those data analysts use. Here’s a look at how they compare.

Types Of Data Scientist

Graphic Designing Tools

SCOPE

Digital marketing helps you reach a larger audience than you could through traditional methods, and target the prospects who are most likely to buy your product or service. Additionally, it’s often more cost-effective than traditional advertising, and enables you to measure success on a daily basis and pivot as you see fit.

FAQS

Can a data analyst become a data scientist?‎

Should I study data analytics or data science?‎

Do data analysts code?‎

Is data analytics a good career?‎

KEY FEATURES

A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations

Python
Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include:

Excel
Data visualization: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work:

Concepts of Business and Data understandings

Introduction to Data Analytics , Phases of Data Analytics Projects, Improve your Analytical Thinking, Data Modeling, Model Evaluation, and Module Development