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WHAT DO I NEED TO BECOME A DATA SCIENTIST?
- Typically an undergraduate degree in computer science, social science or one of the physical sciences with a grounding in statistics followed by a Master’s degree or PhD in data science or a related field.
- Proficiency in an analytics tool like R (https://www.r-project.org/) that is used to solve statistical problems and manipulate data.
- Skilled in a programming language like Python, Java, Perl, or C/C++. (About 40% of data scientists use Python as their main programming language.)
- Other skills such as familiarity with Apache Hadoop (https://hadoop.apache.org/) , Hive (https://hive.apache.org/), Pig (https://pig.apache.org/), and Spark (https://spark.apache.org/) – all tools to process and analyze large data sets, and cloud tools like Amazon S3 (https://aws.amazon.com/s3/) is also an advantage.
- Understanding machine learning and how artificial neural networks, reinforcement learning, and other machine learning techniques work.
- Visualising data with various visualisation tools like Tableau, D3.js, Matplottlib, and ggplot will be important skills to have as a data scientist.
- Naturally curious and a drive to want to find out “why” things are the way they are.
- Organized – You need to be able to organize data and results when handling lots of data points from many different sources.
- Critical thinker – It’s one thing to put data points on a chart it’s another thing to creatively and critically evaluate what it means and how it all fits together in the larger scheme of things.
- Focus – Working with data requires focused attention, often for long stretches of time, and an attention to detail without quickly getting board if the magical revelations don’t jump out at you immediately.
- Good communication – To get data, collaborate with others and communicate results to your employer, your communication skills need to be strong. To be able to communicate in a non-technical way to the company you are working for and give non-technical summaries and ideas of opportunities will be a key skill to have.
[/vc_column_text][kleo_divider text=”Articles”][vc_basic_grid post_type=”post” max_items=”10″ element_width=”12″ initial_loading_animation=”none” grid_id=”vc_gid:1546556571062-b302facdcd7ee155a77a0726aeb17577-3″ taxonomies=”15290″][/vc_column][vc_column width=”1/3″][kleo_divider text=”Education”][vc_column_text][/vc_column_text][vc_column_text]Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons (Video Training)
4 Hours of Video InstructionCreate an end-to-end data analysis workflow in Python using the Jupyter Notebook and learn about the diverse and abundant tools available within the Project Jupyter ecosystem. OverviewThe Jupyter Notebook is a popular tool for learning and performing data science in Python (and other languages used in data science). This video tutorial will teach you about Project Jupyter and the Jupyter ecosystem and get you up and running in the Jupyter Notebook environment.
Data Science Fundamentals Part 1, Complete Video Course: Learning Basic Concepts, Data Wrangling, and Databases with Python
20 Hours of Video InstructionData Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist.
[/vc_column_text][vc_column_text][/vc_column_text][vc_text_separator title=”Books”][vc_column_text]Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage
Big Data is a big topic, based on simple principles. Guided by leading expert in the field, David Stephenson, you will be amazed at how you can transform your company, and significantly improve KPIs across a broad range of business units and applications.
Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
Now, a leader of Northwestern University’s prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.[/vc_column_text][/vc_column][vc_column width=”1/3″][kleo_divider text=”Jobs”][vc_column_text][ziprecruiter_jobs search=”Data Science”][/vc_column_text][/vc_column][/vc_row]