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Skillfloor's Data Science Course is designed to equip aspiring data scientists with the essential skills and knowledge needed to excel in the field. With a comprehensive curriculum covering a wide range of topics relevant to data science, this course offers a solid foundation for individuals looking to kickstart or advance their careers in this growing industry. Students can expect to delve into key areas such as machine learning, data analysis, data visualization, and statistical modelling, among others. Through hands-on projects and real-world case studies, participants will gain practical experience and develop the expertise required to tackle complex data challenges effectively. Upon completion, graduates will receive a recognized data science certification, validating their proficiency and enhancing their prospects in the competitive job market.

This  Data Science Course stands out as a top choice among courses for data science enthusiasts seeking a comprehensive and practical learning experience. With a focus on delivering in-demand skills and expertise, this course covers all essential aspects of data science, ensuring students are well-prepared for success in the field. From mastering programming languages like Python and R to understanding advanced techniques in data manipulation and analysis, participants will gain a deep understanding of the data science environment. Moreover, the course offers flexibility for learners of all levels, making it suitable for beginners and experienced professionals alike. Whether one is aiming to transition into a data science career or enhance their existing skill set, Skillfloor's Data Science Course provides the necessary training and certification to excel in 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: An overview of data science, explaining its importance and how it's used across different industries to solve problems and make decisions.

  • Python Programming: Focus on Python, a popular programming language in data science, covering basics to advanced concepts necessary for data analysis and manipulation.

  • Data Analysis and Visualization: Instruction on using tools and libraries like Pandas and Matplotlib to analyze and visualize data, making it easier to understand trends and patterns.

  • Machine Learning Fundamentals: A look at the core concepts of machine learning, including supervised and unsupervised learning, and how to apply them to real-world scenarios.

  • Deep Learning Basics: An introduction to deep learning, discussing neural networks and how they can be used for more complex data analysis and predictions.

  • Natural Language Processing (NLP): Teaching how to work with textual data, enabling machines to understand human language, with applications in chatbots and sentiment analysis.

  • Big Data Technologies: Overview of technologies used to handle large datasets that cannot be processed with traditional databases, including Hadoop and Spark.

  • Data Ethics and Privacy: Discussion on ethical considerations and privacy concerns in data science, emphasizing responsible use of data.

  • Project Management for Data Science: Insights into managing data science projects effectively, including planning, execution, and delivery within timelines.

  • Capstone Project: A practical project that allows students to apply what they've learned to solve a real-world problem, demonstrating their skills to potential employers.



After you complete the Data Science Developer course with Skillfloor, you will receive a certification from Skillfloor. This certification serves as proof of your successful completion of the course and signifies your proficiency in data science development skills, including programming, data manipulation, data analysis, and more. This credential can be valuable for your career in data science, as it demonstrates your competence in handling data-related tasks and programming in the field. Additionally, it may enhance your job prospects and credibility in the data science job market

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Tools Covered

Python, Matplotlib, TensorFlow, PyTorch, toolstools_covered Python, Matplotlib, TensorFlow, PyTorch, tools

Top 10 reasons

  1. Certification: Become certified as a Data Science Developer and show your expertise in creating and implementing data-driven solutions.

  2. Fundamental: Learn fundamental abilities in data processing, machine learning, and programming. These are necessary for creating scalable and reliable data science solutions.

  3. Practical learning: Through real projects and coding exercises, you may hone your skills in creating effective data-driven apps.

  4. knowledge: Learn from top professionals in the field who have practical expertise, and obtain knowledge of the newest methods and instruments in data science development.

  5. Career: Boost your chances of landing a job by learning highly sought-after skills that companies use to drive innovation and expansion value.

  6. Community: Become a part of a peer-led and mentor-supported learning community that promotes cooperation and information exchange in data science development processes.

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

  8. Information and tools: Get access to state-of-the-art information and tools, like as development frameworks and software libraries, to enhance your comprehension of data science development principles.

  9. Join a network: Join a network of data science developers and experts to broaden your horizons, find new prospects for employment, and establish collaborative projects.

  10. knowledge and abilities: Equipped with the knowledge and abilities to develop data-driven apps that propel company success, get ready to thrive in data science development roles.




  •  Overview of Data Science and its Importance

  •  Key Applications and Case Studies in Various Industries

  •  Ethics in Data Science: Privacy, Bias, and Fairness

  •  Setting Up the Data Science Environment (Python/R, Jupyter Notebooks, etc.)

  •  Python/R for Data Science: Syntax, Control Structures, and Data Structures

  •  Working with Libraries: NumPy, pandas, matplotlib, and seaborn for Python; dplyr, ggplot2 for R

  •  Basics of Algorithm Complexity and Efficient Coding Practices

  •  Data Collection Methods and Sources

  •  Data Cleaning and Preprocessing Techniques

  •  Handling Missing Data, Outliers, and Duplicate Records

  •  Data Transformation and Feature Engineering

  •  Descriptive Statistics and Data Summarization Techniques

  •  Data Visualization Techniques and Best Practices

  •  Exploring Relationships between Variables

  •  Introduction to Geographic Data Analysis and Visualization

  •  Supervised vs. Unsupervised Learning

  •  Basic Concepts: Overfitting, Underfitting, Cross-Validation

  •  Simple Linear Regression, Logistic Regression, and Decision Trees

  •  Clustering, PCA, and K-Means Clustering

  •  Advanced Regression and Classification Techniques

  •  Ensemble Methods: Random Forests, Gradient Boosting

  •  Introduction to Neural Networks and Deep Learning

  •  Model Evaluation Metrics and Techniques

  •  Introduction to Big Data and Its Challenges

  •  Overview of Hadoop, Spark, and Their Ecosystems

  •  Working with Big Data in a Distributed Environment

  •  Real-time Data Processing and Analysis

  •  Advanced-Data Visualization Techniques

  •  Interactive and Web-based Visualizations with Tools like Tableau, Power BI, or Dash for Python

  •  Storytelling with Data and Effective Communication Strategies


A certified data science developer is someone who has completed a recognized certification program in data science, demonstrating their proficiency in analyzing data, building models, and extracting insights to drive decision-making.


Obtaining certification in data science can validate your skills, enhance your credibility, and increase your job prospects in fields where data analysis and interpretation are essential.

To become certified, you typically need to complete specific training courses, gain hands-on experience with data science tools and techniques, and pass an exam that tests your knowledge of data science concepts.

Skills such as proficiency in programming languages like Python or R, knowledge of statistical methods and machine learning algorithms, experience with data visualization tools, and ability to interpret data are important for certification.


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

Earning a data science certification can provide you with valuable skills and credentials that can help advance your career and open up new opportunities in the field of data analytics.


The time it takes to become certified varies depending on the program and your prior knowledge, but it typically takes several months to a year of study and preparation.


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