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Data Science Self-paced Bootcamp with Coaching

The data science bootcamp is a job-ready training that truly masters you in the data science field. The Bootcamp program is rigorous and packed with challenges covering concepts, theories, projects & live coaching sessions.

Self-Paced

Learning Style

BootCamp

Learning Style

Beginner

Difficulty

Varies

Course Duration

Certificate

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The data science bootcamp is a job-ready training that truly masters you in the data science field. The Bootcamp program is rigorous and packed with challenges covering concepts, theories, projects & live coaching sessions.

Course Information

About this Bootcamp:

The data science bootcamp is a job-ready training that truly masters you in the data science field. The Bootcamp program is rigorous and packed with challenges covering concepts, theories and projects. You will also have access to Coaching Sessions, conducted by industry experts, allowing you to excel in the course of this bootcamp.

This is a program in which students are expected to spend 15 to 25 hours a week to master the material. Graduates of this program will learn critical skills for data analytics related jobs.

 

Bootcamp Objectives:

The field of data science is quite dynamic, and the required skills are changing quite rapidly. Companies like Apple, Walmart, Amazon, Exxon Mobil and a lot more besides are looking for professionals to help them strengthen their capacities in diversified data analytics tools and coding languages. To become eligible for a job at these companies, it is crucial to know which skills are in demand. Our bootcamp program is scrutinize and design with the right combination of tools and skills as listed below.

  • Transact-SQL
  • Excel
  • Power BI
  • Data Visualization
  • Python
  • R Programming
  • Machine Learning
  • Spark
  • Azure HDInsight
  • Predictive Analytics

Jobs Roles:

Here is a list of real-world jobs that you can apply this bootcamp to:

  • Data Analyst
  • Data Engineer
  • Data Scientist
  • Business Analyst
  • Data Analytics Engineer
  • Business Intelligence Analyst
  • Business Insights Analyst

Audience:

Are you someone who is passionate about solving real life problems that are data driven? Are you someone who believes the future of better decision-making lies in the right data? If yes – or if any of the following applies to you - this Bootcamp is the smart career move for you:

  • You recently graduated from college and enjoyed math and statistics
  • You have a college degree and currently considering career change but do not know where to start
  • You want to learn how large organizations make data-driven real life social and business decisions
  • You are comfortable with basic data tools like Excel and fundamentals of statistics
  • You are looking for a career that provides tremendous freelancing opportunities and growth

Prerequisites:

  • To ensure your success in this bootcamp , you should have experience with basic computer user skills, be able to complete tasks, be able to search for, browse, and access information on the Internet, and have basic knowledge of computing concepts.

Career & Salary Insight

Outline

More Information

More Information
Subjects Big Data
Lab Access No
Learning Style Self-Paced Learning
Learning Type BootCamp
Difficulty Beginner
Course Duration Varies
Language English

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Course Expert:

Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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