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


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


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.


 Introduction to data science and its importance
 Understanding the role of a certified data science developer
 Overview of the data science lifecycle

 Introduction to Python for data science
 Data types, variables, and control structures in Python
 Data structures in Python  lists, tuples, and dictionaries
 Advanced Python concepts including functions and modules
 Numpy and Pandas for data manipulation

 Probability and Statistics for data science
 Exploratory Data Analysis (EDA)
 Data visualization using Matplotlib and Seaborn
 Introduction to Machine Learning

 Supervised Learning algorithms including Linear Regression, Logistic Regression, KNearest Neighbors, Support Vector Machines, Decision Trees, Random Forest
 Unsupervised Learning algorithms including KMeans Clustering, Hierarchical Clustering, and Dimensionality Reduction
 Model selection and evaluation

 Introduction to Neural Networks
 Convolutional Neural Networks for image classification
 Recurrent Neural Networks for sequence prediction
 Transfer learning and Finetuning pretrained models

 Introduction to NLP
 Text Cleaning and Preprocessing
 Text Classification using Naive Bayes and SVM
 Topic modeling using Latent Dirichlet Allocation

 Introduction to Big Data and Apache Hadoop
 Working with HDFS and MapReduce
 Introduction to Spark and its components
 Processing Big Data using Spark

 Legal and ethical issues in data science
 Data privacy and security
 Communicating insights and results of data science projects

 The capstone project involves implementing the skills learned in the course to solve a realworld data science problem. Students will work on it in teams, under the guidance of a mentor. The project includes data preprocessing, exploratory data analysis, model selection and evaluation, and presentation of results.

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