+91 8805322016 info@easydataschool.com

Data Scientist

Data Scientist

Enroll Now For Free Demo
data-analyst-science near Pune
Recorded content
Of Total 10 Hrs.
data-analyst-science near Pune
Duration
1 day (6.5 hours)
data-analyst-science near Pune
LIVE sessions
4 Workshops
data-analyst-science near Pune
Hands-On Learning
With Practice Modules
data-analyst-science near Pune
Certificate
With License

Overview

How does data science fit into high-value business analytics? What are the differences between Machine Learning (ML) and Artificial Intelligence (AI)? This live, online Executive Overview of Data Science training teaches participants how data science fits into the overall organizational landscape, demystifies data science buzzwords, compares data science programming languages like R and Python, and more. Participants are given a thorough overview of data science concepts and complete hands-on exercises with their instructor.

Objective

  • Understand how data science fits into the existing landscape of traditional organizations
  • Place the phrase ‘data science’ in the broader context of implementing high-value analytics
  • Describe the changing data environment that has motivated this shift
  • Understand the definitions and intuition of key elements of data science such as machine learning and distributed computing
  • Differentiate machine learning from deep learning/AI techniques
  • Contrast the differences and similarities of open-source analytic solutions like R and Python with commercial software such as SAS and SPSS
  • Identify the different roles and related skillsets required to implement high-value data science workflows from a team management perspective
Enroll Now For Free Demo

Outline

  • • New data sources and new demands on data insight
  • • The democratization of data science tools
  • • What changed in the past 10 years; why ‘data science’?
  • • Coming up with definitions for data science: operational and conceptual
  • • How does data science differ from ‘traditional’  reporting?
  • • Is big data the right data?
  • • Building the right data infrastructure
  • • Data versus insights, interesting reports versus high-value products
  • • Defining value in data science products
  • • The cost of low-value data science
  • • The typical data science team
  • • Integrating human-centered design principles to increase the value of these products

  • • P-values and hypothesis testing
  • • Correlation versus causation, observational versus experimental data
  • • Multivariable modeling approaches to explain the relationship between inputs and outputs
  • • Assumptions for causal inference and associated interpretation
  • • Bayesian modeling: turning the traditional paradigm around

  • • Clustering versus Supervised models
  • • Classification versus Regression
  • • Regression example in-depth with example code
  • • Validation strategies for avoiding overfitting, understanding model capacity
  • • Different families of algorithms: high-level overview
  • • Classification example in-depth with example code
  • • Understanding accuracy: what do these measures mean?
  • • Clustering in-depth: use cases and explaining output
  • • Clustering on treatment effects: does the exposure cause a different reaction in different people?

  • • What is a neural network? How is it different from other ML?
  • • Artificial feed-forward neural networks and applications
  • • Neural networks for time series data (recurrent neural networks and convolutional neural networks)
  • • Neural networks for natural language processing
  • • Predictive modeling for image classification

  • • Traits of high performing (and low performing) organizational analytic cultures
  • • What cultural shifts are required for your department?
  • • Roles on the data science team:
    • o Data architects and engineers (organize, move, and store data)
    • o Data managers (extract and transform data for use)
    • o Analysts/statisticians (answer questions using data for insight)
    • o Topical experts (subject matter experts)
  • • Identify roles/skillsets for each of these workflows
  • • Combining these skills and roles into a single team
  • • Training trajectories for core members of these teams (who needs what)
  • • Hiring strategies to build successful data science teams
  • • Developing training opportunities for staff doing work in data science
  • • Hardware/software infrastructure required

Training Materials

All Data Science Overview training students receive comprehensive courseware.

Software Requirements

Detailed setup will be provided upon request.

Why Online Bootcamps

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Learn by working on real-world problems

Capstone projects involving real world data sets with virtual labs for hands-on learning

Learn from experts active in their field, not out-of-touch trainers

Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.

Structured guidance ensuring learning never stops

24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

FAQs

    Data science is a broad field that involves dealing with large volumes of data to uncover hidden trends and patterns and extract valuable information that aids in better decision-making. Companies that collect massive amounts of data use various data science tools and techniques to build predictive models. Simplilearn’s Data Science training can help you learn all its concepts from scratch.

    In a Data Science course, you will learn about many concepts if you are a beginner or an intermediate. This training program is around six to twelve months, often taken by industry experts to help candidates build a strong foundation in the field. Besides the theoretical material, our Data Science course includes virtual labs, industry projects, interactive quizzes, and practice tests, giving you an enhanced learning experience.

    A Data Scientist is an individual who gathers, cleans, analyzes, and visualizes large datasets to draw meaningful conclusions and communicate them to business leaders. The data is collected from various sources, processed into a format suitable for analysis, and fed into an analytics system where statistical analysis is performed to gain actionable insights.

    These Data Science courses, co-developed with IBM, will give you an insight into Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. This Data Science training will teach you R, Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics with SAS, data visualization with Tableau, and an overview of the Hadoop ecosystem. You will earn an industry-recognized certificate from IBM and Simplilearn that will attest to your new skills and on-the-job expertise.

    Professionals with no prior knowledge of the field can easily begin with this Data Science course, as you’ll gain a thorough knowledge of the basic concepts as well.

    Yes, this Data Science course is suitable for recent graduates and experienced professionals willing to start a career in data science.

    There are no specific eligibility requirements to take this Data Science training.