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The Data Science Course offered by Skillfloor is a comprehensive program designed to equip individuals with the necessary skills and knowledge to excel in the field of data science. This course covers a wide range of topics essential for aspiring data scientists, including statistical analysis, machine learning algorithms, data visualization, and programming languages such as Python and R. With a focus on practical applications and real-world projects, students gain hands-on experience working with data sets and solving complex problems. Upon completion of the course, participants receive a data science certification, validating their expertise and readiness to tackle data-driven challenges in various industries.

Aspiring data scientists can expect to delve into topics such as data preprocessing, predictive modelling, and data mining techniques, all essential for analyzing and interpreting large datasets. Upon successful completion, graduates not only receive a data science certificate but also gain access to a supportive community of data enthusiasts and experts. Whether you're looking to kickstart a career in data science or advance your existing skills, Skillfloor's Data Science Course provides the necessary tools and knowledge to become a proficient data science associate ready to tackle the challenges of the current data world.




Skill Level



1-month Unpaid

Live Project




Live Training


Career Assistance


Expiry Period

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Course Highlights

  • Introduction to Data Science: A foundational overview that explains the field of data science, including its significance and how it's applied across different industries.

  • Programming Basics: Focuses on teaching the essentials of programming languages relevant to data science, such as Python or R, including syntax and basic operations.

  • Data Manipulation and Analysis: Covers techniques for handling, cleaning, and analyzing data effectively using tools like pandas in Python.

  • Statistics for Data Science: Offers a primer on statistical concepts crucial for interpreting data, including probability, regression, and hypothesis testing.

  • Machine Learning Fundamentals: Introduces the core principles of machine learning, including supervised and unsupervised learning techniques.

  • Deep Learning Basics: Provides an understanding of deep learning concepts and how they're applied in areas such as image and speech recognition.

  • Data Visualization: Teaches methods for presenting data visually using libraries like Matplotlib and Seaborn in Python, making findings more understandable.

  • Big Data Technologies: Explores technologies used for handling large datasets that are beyond the capacity of traditional databases, including Hadoop and Spark.

  • Ethics and Data Privacy: Addresses the ethical considerations and privacy laws relevant to data science, emphasizing responsible data handling.

  • Capstone Project: A practical project that allows students to apply what they've learned by tackling a real-world data science problem, reinforcing their skills and knowledge.



The Certified Data Science Associate certificate equips individuals with foundational data science skills. Covering key concepts in statistics, programming, and data analysis, this certification validates a practical understanding of data science principles. Ideal for beginners, it fosters competence in handling and interpreting data. A valuable asset for anyone aiming to kickstart a career in data science or enhance their analytical capabilities.

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Top 10 reasons

  1. Certification: Get recognized by earning a Data Science Associate certification, which attests to your ability to extract value and insights from data.

  2. Fundamental abilities: Acquire fundamental abilities in data analysis, machine learning, and data visualization. These are necessary for making decisions based on data.

  3. Practical projects: Through practical projects and case studies, you can get experience that will help you improve your ability to apply data science techniques to address challenging challenges.

  4. Best practices: Get insights into the newest trends and best practices in data science by learning from top industry professionals with practical expertise.

  5. Skills: Improve your chances of landing a job by learning highly sought-after skills that employers in a variety of sectors value.

  6. learning community: Become a part of a peer-led and mentor-supported learning community that promotes cooperation and knowledge exchange regarding data science techniques.

  7. Individualized advice: Experienced instructors will provide you with individualized advice and feedback so that your learning process is customized to your goals.

  8. Information and tools: Get access to state-of-the-art information and tools, including analytics platforms and programming languages, to enhance your understanding of data science ideas.

  9. Employment: As you participate with other data science experts and hobbyists, you'll be able to extend your network and find chances for cooperation and employment.

  10. knowledge and abilities: With the knowledge and abilities to promote corporate success through data analysis and extract actionable insights, you are ready to flourish in data science roles.


  •  Overview of Data Science

  •  Key Concepts and Terminology

  •  The Data Science Process

  •  Applications of Data Science in Industry

  •  Data Types and Structures

  •  Data Collection Methods

  •  Data Cleaning and Preprocessing

  •  Exploratory Data Analysis (EDA)

  •  Descriptive Statistics

  •  Inferential Statistics

  •  Probability Distributions

  •  Hypothesis Testing

  •  Introduction to Python/R for Data Science

  •  Libraries and Tools (NumPy, Pandas, Matplotlib, etc.)

  •  Data Manipulation and Analysis

  •  Introduction to SQL for Data Querying

  •  Introduction to Machine Learning

  •  Supervised vs. Unsupervised Learning

  •  Regression Analysis

  •  Classification Techniques

  •  Decision Trees and Random Forests

  •  Support Vector Machines

  •  Clustering Techniques

  •  Neural Networks and Deep Learning Basics


  •  Principles of Effective Visualizations

  •  Tools for Data Visualization (Tableau, Power BI, etc.)

  •  Storytelling with Data

  •  Communicating Data Science Findings



A certified data science associate is someone who has completed a recognized certification program in data science, showing their ability to understand and apply foundational concepts in data analysis.

Certification in data science validates your skills, boosts your credibility, and enhances your employability in data-related fields.


To get certified, you typically need to complete specific training courses, gain practical experience, and pass an exam that tests your knowledge of data science fundamentals.


Skills include proficiency in programming languages like Python or R, understanding of statistical methods, data visualization techniques, and knowledge of machine learning algorithms.


With certification, you can pursue roles such as data analyst, business analyst, data engineer, or machine learning engineer in various industries.


It varies but typically takes several months to a year of study and practice to get certified.


It can lead to better job prospects, higher salaries, and opportunities for career advancement.

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