1 / 10

A Beginner-Friendly Guide to Starting a Career in Data Science

This beginner-friendly guide breaks down the steps to start a career in data science, covering essential skills, certifications, hands-on experience, and job readiness. Ideal for anyone looking to enter the field and build a strong foundation in data science.

Vamsi26
Download Presentation

A Beginner-Friendly Guide to Starting a Career in Data Science

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Beginner-Friendly Guide to Starting a Career in Data Science www.iabac.org

  2. Why Choose Data Science? Data science offers a powerful blend of technology, problem-solving, and impact. It helps turn data into meaningful insights, drives smarter decisions, and opens doors to high-demand careers across industries like healthcare, finance, tech, and more. www.iabac.org

  3. What Does a Data Scientist Do? Gather and organize large datasets Analyze patterns and trends Build predictive models Visualize and report insights Collaborate with teams to solve business problems www.iabac.org

  4. Build Your Educational Foundation A formal degree isn’t mandatory, but backgrounds in computer science, statistics, or engineering help. Focus on learning probability, basic statistics, and programming languages like Python or R. Strong data cleaning and exploration skills are essential to start your data science journey. www.iabac.org

  5. Core Skills for a Certified Data Scientist Programming: Python, SQL Libraries: Pandas, NumPy, Scikit-learn Data visualization: Tableau, Matplotlib Machine Learning fundamentals Working with real datasets (Kaggle, UCI) www.iabac.org

  6. What Will You Learn? Data visualization: Tableau, Power BI Programming & Stats: Python, R Machine Learning fundamentals Big Data: Hadoop, Spark SQL & data management Business intelligence & automation tools www.iabac.org

  7. Choosing the Right Data Science Certification Look for: Industry recognition (e.g., IABAC, IBM, Google) Practical projects and case studies Flexibility in learning schedule Career support options Benefits: Credibility boost Portfolio development Better job prospects www.iabac.org

  8. How to Choose the Right Course Accreditation: Local & international recognition Updated curriculum: Tools, case studies, real-world relevance Industry-experienced instructors Internship & placement support Online/part-time options for working professionals www.iabac.org

  9. Gaining Real-World Experience Personal projects with public datasets Internships or freelance gigs Kaggle competitions for hands-on practice Contributing to open-source projects Build a GitHub portfolio www.iabac.org

  10. Thank You www.iabac.org

More Related