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People Decode

Transform Your Career with Our Expert Led Data Science Program

Explore the World of Data Science: Skills, Tools, and Techniques

    Data Scientist Certification

    PeopleDecode’s comprehensive data science course is designed by industry experts to provide learners with the skills and knowledge necessary to become proficient data scientists. The curriculum covers the entire data science workflow, from data collection and cleaning to advanced machine learning techniques and data visualization.

    Data science course involves focusing on the most essential topics and skills, ensuring hands-on practice, and leveraging resources effectively to expedite learning and develop advanced data science solutions tailored to organizational needs.

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    Course Details

    Course Fees

    ₹ 46,000/-

    40,000/- + Tax

    Top Companies Hiring Data Scientist in India

    Explore New Horizons with Us

    Career Opportunities

    • Data Scientist
    • Business Intelligence Analyst
    • Data Engineer
    • Data Consultant
    • Data Analyst
    • Statistician
    • Data Architect
    • Machine Learning Engineer
    • Big Data Engineer
    • AI research Scientist

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    Certification and Career Support

    Certification: Data Scientist Certification
    Empower Your Career with PeopleDecode's course completion Certificate
    Join our Global Talent Ecosystem for lifelong career support and connections with diverse partners. Access opportunities across industries, backed by endorsements from industry experts. We're committed to your professional growth and success. Start your journey towards fulfilling career milestones today.

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    Certification

    Data Scientist Certification
    Empower Your Career with PeopleDecode's course completion Certificate

    Career Support

    Join our Global Talent Ecosystem for lifelong career support and connections with diverse partners. Access opportunities across industries, backed by endorsements from industry experts. We're committed to your professional growth and success. Start your journey towards fulfilling career milestones today.

    For More Details Connect Our Team

    FAQs

    Data science is an interdisciplinary field that combines statistics, computer science, and domain knowledge to extract insights from structured and unstructured data. It involves processes like data collection, data cleaning, data analysis, and the use of machine learning algorithms to build predictive models.

    • Programming: Proficiency in languages such as Python, R, SQL.
    • Statistics and Mathematics: Strong understanding of statistical methods and mathematical concepts.
    • Machine Learning: Knowledge of algorithms and model building.
    • Data Manipulation and Analysis: Using tools like pandas and NumPy.
    • Data Visualization: Using libraries like Matplotlib, Seaborn, and tools like Tableau, Power BI.
    • Big Data: Experience with Hadoop, Spark, and other big data technologies.
    • Domain Knowledge: Understanding of the industry-specific data and requirements.
    • Programming Languages: Python, R, SQL
    • Data Analysis Tools: pandas, NumPy
    • Machine Learning Libraries: scikit-learn, TensorFlow, Keras, PyTorch
    • Big Data Technologies: Hadoop, Spark
    • Data Visualization Tools: Matplotlib, Seaborn, Tableau, Power BI
    • Database Management Systems: MySQL, PostgreSQL, MongoDB
    • Data Collection: Gathering data from various sources.
    • Data Cleaning: Removing inaccuracies and inconsistencies in the data.
    • Data Analysis: Exploring data to find patterns and insights.
    • Model Building: Creating machine learning models to predict future trends.
    • Data Visualization: Presenting data in a clear and concise manner.
    • Communication: Explaining findings and recommendations to stakeholders.
    • Technology: Internet companies, software development.
    • Finance: Banking, investment, insurance.
    • Healthcare: Medical research, pharmaceuticals, healthcare providers.
    • Retail: E-commerce, supply chain management.
    • Telecommunications: Network optimization, customer analytics.
    • Automotive: Autonomous driving, predictive maintenance.
    • Government: Public policy, national security.
    • Educational Background: Obtain a degree in computer science, statistics, mathematics, or a related field.
    • Online Courses and Certifications: Platforms like Coursera, edX, Udacity offer data science courses and specializations.
    • Practical Experience: Engage in projects, internships, and participate in competitions like those on Kaggle.
    • Build a Portfolio: Showcase your projects and skills on platforms like GitHub.
    • Networking: Join data science communities and attend industry events and meetups.
    • Books:
      • "Python for Data Analysis" by Wes McKinney
      • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
      • "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    • Online Courses:
      • Coursera: Data Science Specialization by Johns Hopkins University
      • edX: MicroMasters Program in Data Science by UC San Diego
      • Udacity: Data Scientist Nanodegree
    • Websites and Blogs:
      • Towards Data Science
      • Analytics Vidhya
      • Kaggle
      • Medium
    • Data Analyst: Primarily focuses on analyzing data to generate insights and reports. They use tools like Excel, SQL, and visualization tools to interpret data.
    • Data Scientist: Goes beyond analysis to build models and algorithms to predict future trends. They require deeper knowledge in programming, machine learning, and big data technologies.
    • Data Quality: Ensuring data is clean and accurate.
    • Data Integration: Combining data from various sources.
    • Scalability: Handling large volumes of data efficiently.
    • Model Deployment: Integrating machine learning models into production systems.
    • Interpreting Results: Making sure results are understandable and actionable for stakeholders.
    • Automated Machine Learning (AutoML): Simplifying the process of building machine learning models.
    • Explainable AI: Making AI models more interpretable and transparent.
    • Edge Computing: Processing data closer to where it is generated.
    • AI Ethics: Ensuring ethical considerations are integrated into AI systems.
    • Integration with IoT: Analyzing data generated by IoT devices for real-time insights.

    Hear from our Learners

    Data Science Certification Course offered a great mix of theory and practical application. The live classes were interactive, and the instructors were knowledgeable. The projects were challenging but manageable, and I now feel prepared to tackle data science challenges in my current role."
    Megha M
    Data Camp's interactive courses made learning data science fun and engaging. The bite-sized lessons were perfect for my busy schedule, and the practical exercises helped reinforce the concepts. I particularly enjoyed the machine learning courses, which were very well structured."
    Prince M
    "General Assembly's Data Science Immersive was intense but worth every minute. The curriculum was up to date with industry standards, and the career coaching sessions were invaluable. I appreciated the emphasis on both technical skills and soft skills like communication and teamwork."
    Sumathi P
    "The mentorship model at PeopleDecode was incredibly effective. Having an industry expert guide me through complex topics and provide feedback on my projects made a huge difference. The job assistance was a safety net that gave me the confidence to invest in the program."
    Ashwini
    Data Science course with PeopleDecode was a game-changer for my career. The project-based learning approach was exactly what I needed. I could apply what I learned immediately to real-world problems, and the career support services helped me land a job as a junior data scientist."
    Bindu
    Data Science courses were challenging but very rewarding. The in-depth focus on Python programming and statistical concepts has given me a solid foundation. I now feel confident in tackling data analysis projects at work and have even started contributing to open-source projects."
    Sathyan
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    Trainers Profile

    Shaghil Jamal

    Data Science Consultant and Industrial Trainer

    Experienced data science consultant and industrial trainer specialized in machine learning, deep learning, data analytics and visualization. Skilled in designing custom solutions (curriculum, syllabi, delivery calendar) and delivering high-quality training programs to improve data literacy and analytical skills.

    Team Building

    Objective

    This training program will help develop shared vision/understanding for a high-performing team, determining the critical elements and individual contributions that comprise the vision in their organizations. It will also promote practice on key skills needed to address the inevitable challenges that arise in teams, notably, appreciating individual differences, communicating collaboratively, and managing conflict.

    Training Methodolgy

    Training program with scenario based case studies, activities & debriefing.

    Key Take Aways

    1. Be able to describe: 

    • Characteristics of effective teams

     • Four stages of team development

     • How individual differences and roles contribute to building a strong team 

    2. Have practiced skills in: 

    • Supporting team development through its formative stages

     • Constructive communication

     • Conflict resolution 

    3. And will have discussed: 

    • A vision of their ideal team 

    • Principles and behaviors to guide team performance

     • A plan for monitoring progress toward achieving their vision


    Curriculum & Pricing
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