Data Science Bootcamp: Data Science Certification Courses

Overview

Data is at the heart of nearly every aspect of our modern lives. From smartphones and social media to mobile banking and cybersecurity, our world is growing increasingly digital – and that means an increased reliance on data, data integrity, and data science. The job market has responded in kind – according to the Bureau of Labor Statistics, data science is one of the nation’s fastest-growing occupations, with an anticipated growth rate of 31% through 2029.

However, the data landscape is evolving. The workforce needs more people who can work responsibly with data, leverage the power of artificial intelligence, and build visualizations that are accessible to everyone to help the business achieve its goals. Technological advancements are moving quickly, and, quite simply, there are not nearly enough people equipped with the skills needed to fill the open data professional positions.

With XCEED Data Science Bootcamp, you can become the type of professional that organizations need to make smarter, data-driven business decisions. This data science bootcamp program is designed to take you from little or no experience to a ready-to-hire data professional by providing you with the most up-to-date skills and hands-on experience that companies look for.

Take advantage of a market-driven curriculum, enrol in the data science certification course that’s designed to close the skills gap and gain an edge in a competitive, rapidly-growing job market.

Industry Certifications

Data Analyst Associate
IBM Data Analyst Professional Certificate
IBM Data Science Professional Certificate

In the 460-hour Data Science and Analytics Bootcamp, you will attend lectures, take part in individual and group exercises, and gain access to virtual labs and real-world projects that teach you how to use data to gain business insight while using data analytics and data science best practices. You will complete three experiential training projects in the data science bootcamp that you can showcase on your personal portfolio when applying to jobs in the field. Classes are taught by instructors who are leaders in the industry and who bring a wealth of knowledge and experience to the learning environment. You will benefit from your instructors’ current industry expertise as well as from their unique insider’s understanding of the fast-paced field of data science and analytics.

The Program structure:

Introduction Course
Professional Bootcamp

Data Driven Story Telling
Data Wrangling and Analytics
Data Science and Business Intelligence

Capstone Project

This Program is Perfect For

The accelerated Data Science Bootcamp is built for learners of any professional background who have a strong affinity for technical solutions, enjoy aspects of conceptual and visual design, and seek creative ways to solve problems.

The goal of the data science bootcamp is to take you from little or no experience to a ready-to-hire data professional by providing you with the most up-to-date skills and hands-on experience that companies look for in candidates. Data professionals who opt for data science certification courses have many advantages over other job seekers because they can organize business requirements into data requirements, see the big picture, and work on a variety of data ingestion, cleaning, and analysis assignments.

Learning Outcomes:

By the end of the Data Science & Analytics Bootcamp program the student will be able to:

Import data into databases, query data, join data together, filter and sort data, create views, and

export data using SQL.
Understand how statistics and probability are used in business decision making.
Communicate data insights to stakeholders using story points that speak to their audience’s needs
and expectations.
Apply core programming concepts such as expressions, data types, variables, functions, loops,
and arrays.
Use industry-standard software to automate data wrangling processes including sourcing,
curating, and importing data, exploratory data analysis, data cleansing, and data visualization.
Produce effective data visualizations that show the most important parts of data to stakeholders
in a clear and simplified way.
Use SQL programming to code stored procedures, triggers, cursors, and query optimization.
Develop ETL scripts and data pipelines combining the use of SQL and Python.
Develop and apply best practices for reporting, graphs and charts, and dashboards in a way that
can be applied in any business intelligence application.
Understand core concepts and methods used for big data and IoT, including characteristics of big
data, data warehousing, data lakes, data virtualization, and cloud-based data infrastructure
services.
Investigate and apply supervised and unsupervised machine learning algorithms, including
classification, clustering, association rules, and time-series forecasting

Course Curriculum

Module 01

Data-Driven Storytelling

SQL and Databases

This module of the data science bootcamp provides you with an introduction to SQL, a popular language used to query databases. Using SQL, you will import data into databases, query data, join data together, filter and sort data, create views, and export data. Further, this course introduces you to database design and teaches you how to manage your own database.

Statistics and Probability

This module of the data science certification course aims to enlighten you on how statistics and probability are used in business decision-making. This course aids you in building a strong foundation in descriptive statistics, conditional probability, and advanced modeling techniques. We use Microsoft Excel to provide a practical application to theoretical discussions. You develop the ability to approach real-world problems from an analytical perspective with confidence.

Data Storytelling

In this module, you discover the power of a story and how to develop a story arc around your data goals. Successfully communicating data insights depends on the audience of stakeholders and the story points that speak to their needs and expectations. You continue to keep a data story thread throughout the entire data wrangling adventure as you frame your data goals with purpose.

Milestone Project 1: Building and Presenting Data Stories

This milestone project allows you to explore your skills in the areas of statistics, Excel, SQL, and data storytelling. You have the opportunity to demonstrate your ability to clean and manipulate a dataset. Additionally, you perform advanced statistical analysis on the data using summary statistics, linear regression, and modeling. Finally, you put your visualizations and insights into a coherent data story to present to your classmates. The instructional team formally reviews the data analytics mile

Module 02

Data Wrangling and Analytics

Python Programming

In this module, you explore the fundamental concepts of programming and learn how to structure their analyses. Topics include core programming concepts such as expressions, data types, variables, functions, loops, and arrays. Practice your coding skills through building highly structured and maintainable code using Jupyter notebooks.

Data Wrangling

Develop core data wrangling skills by expanding your Python programming skills. Explore a series of data analysis processes from sourcing, curating, and importing data, exploratory data analysis, data cleansing techniques, and data visualization techniques. Next, expand your toolbox by using industry standard software to automate data wrangling processes.

Visual Communications

Explore visual dynamics and principles to produce effective data visualizations that show the most important parts of data to stakeholders in a clear and simplified way.

Advanced SQL Programming

Building upon the skills you gained in the SQL and Databases course, this course extends your skill in SQL programming and covers topics such as stored procedures, triggers, cursors, and query optimization. You also develop ETL scripts and data pipelines combining the use of SQL and Python.

Milestone Project 2: Data Integration, Preparation, Reporting, and Presentation

This milestone project focuses on developing your ability to attain, transform, investigate, and present data throughout a data project life cycle. Demonstrate your ability to build data pipelines and wrangle data into a usable format for downstream data visualization and analytics. Present your reports and findings to classmates and then incorporate the project into your GitHub portfolio. The instructional team reviews projects in this milestone

FAQs

#1. What is the difference between a data science certification course and big data course?

Data Science is a field that includes a variety of tools and techniques for extracting usable information from unstructured data. A data science certification course entails a variety of data modeling methodologies as well as other data-related duties such as data cleansing, preparation, and analysis. Big Data refers to the massive amounts of organized, unstructured, and semi-structured data generated by numerous channels and organizations.

#2. What is the job of a data scientist after completing a data science certification course ?

After completing a data science certification course, data scientists can fulfill a number of job responsibilities. A data scientist’s typical job, on the other hand, can be anything that has to do with arithmetic or statistics. A data scientist’s daily tasks include establishing business challenges or opportunities, manipulating data to solve problems, data modeling and testing to provide business solutions, and coding to implement the chosen solution. Data scientists code in a variety of languages for data science and analysis, such as Python, SAS, R, SQL, and others.

#3. Can I make a future in data science?

Without the use of data science principles, dealing with ever-increasing amounts of data and ever-more complicated business problems becomes extremely challenging. Data science, in this perspective, is here to stay for a long time. With the exponentially growing amount of data, enrolling for a data science certification course can be a brilliant way to become an asset for organizations.

#4. What Do You Need to Know Before You Begin Learning Data Science?

While it is true that learning data science or undertaking a data science certification course may be easier and faster for mathematicians, statisticians, and programmers, this does not indicate that a job in data science is wholly unreachable to persons with different qualifications. To succeed in your studies, you’ll need a fascination with data and what lies beneath it, as well as an exploratory attitude, some creativity, and a strong desire to master data science.

#5. How good at coding should a data scientist be?

Without a question, a person who wishes to work in data science or take up a data science certification course should be conversant with a variety of programming languages and related technical tools, and most firms that hire data scientists do so. A data scientist’s coding arsenal, on the other hand, is far less extensive than that of, example, a software developer or a computer scientist.

Rather than being a purely programming-focused discipline, data science is a broad field of study that necessitates a wide range of skills and competencies in addition to coding, including an analytical mindset, knowledge of statistics, probability, and linear algebra, effective storytelling, and business domain knowledge.

#6. What primarily does a data science certification course encompass?

A data science course typically includes data engineering, statistical analysis, programming and domain expertise.

#7. What are the data science career opportunities?

Believe it or not, data scientists are not one-trick ponies. There are many opportunities for data scientists in careers like:

data engineer
cloud engineer
applications architect
machine learning architect
data architect, and more

#8. What does any typical data science course syllabus include?

Any typical data science course syllabus includes programming with python, statistical inference, data wrangling, storytelling with data, hands-on experience, NoSQL, Spark and more.

#9. How do you check for data quality?

Some common parameters or definition used to check for data quality include accuracy, integrity, conformity, uniqueness, completeness and consistency.

#10. What skills do employers look for in a data scientist?

Employers typically expect a data scientist to have the following technical skills:

A decent understanding of Python or R (especially the popular data science modules of these languages)
Capacity to deal with the command line SQL knowledge
Expertise in statistical principles, data cleansing, wrangling, analysis, and visualization
Machine learning or deep learning algorithms are used for predictive modeling and model estimation.
Using unstructured data to make stories
Web scraping
Debugging

#11. When can I enrol for your data science bootcamp program?

Well, you can enrol for our data science bootcamp anytime you want.