Data Scientist GLOBAL CERTIFICATION Training
Become a globally certified data scientist with hands-on training in machine learning, big data, and AI-driven analytics.
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This course includes
- Live sessions
- One-to-one counseling
- Corporate mentors with 15yrs of experience
- Global certification
- Real-world live projects
- Lifetime support
- Durations: 40 week
- Lectures: 0
- Maximum Students: 20
- Skill level: advanced
- Certificate: yes
- Introduction to Data Science
- Statements and Syntax
- Functions
- Basics of Statistics & Linear & Multiple Regression
- Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
- Unsupervised Learning
- Deep Learning
- Memory management and Garbage collections
- File Operations
- Python Modules for Data Science
- Classification
- Supervised Learning
- Tree-Based Methods
Data Scientist
A data scientist is a professional skilled in extracting valuable insights from vast datasets. Proficient in statistical analysis, programming, and machine learning, they bridge the gap between raw data and informed decision-making.
Utilizing tools like Python, R, and SQL, data scientists clean, preprocess, and analyze data, uncovering patterns and trends. Their role extends to creating predictive models, optimizing algorithms, and communicating findings to non-technical stakeholders. The interdisciplinary nature of data science requires a blend of mathematical acumen, coding expertise, and domain knowledge.
As businesses increasingly rely on data-driven strategies, data scientists play a pivotal role in driving innovation and solving complex problems.
Data scientists use applications like Python, R, and SQL for data analysis, machine learning, and statistical modeling. Tools such as Jupyter Notebooks, TensorFlow, and Tableau are also vital for their workflow.
Building a career in data science involves mastering skills like statistical analysis, machine learning, and data visualization.
Acquire expertise in programming languages like Python, SQL, and R. Gain hands-on experience through projects, collaborate with peers, and stay updated on industry trends to unlock rewarding opportunities in this dynamic and rapidly growing field.
Seize control of your career today by venturing into machine learning. Become a machine learning developer, mastering algorithms, data analysis, and programming. Navigate the dynamic landscape of artificial intelligence, unlocking doors to innovative applications and cutting-edge technology.
With a growing demand for machine learning expertise, this journey empowers you to craft solutions that drive industries forward.
Embrace the challenge, upskill in Python, R-Programming, TensorFlow, or PyTorch, and position yourself as a key player in the evolving world of AI. Take charge, stay ahead, and shape a future where your machine learning skills redefine possibilities and open new avenues of success.
Machine learning applications employ diverse tools like TensorFlow, PyTorch, R-programming and Scikit-learn for image recognition, natural language processing, predictive analytics, and more. These frameworks empower developers to create intelligent systems, enhancing automation, decision-making, and problem-solving across various domains.
Data Scientist Course Curriculum
- Overview of data science, its applications, and career paths
- Understanding the data science workflow
- Key roles: Data Analyst vs. Data Scientist vs. Data Engineer
- Tools and technologies used in data science
- Data collection methods and sources
- Data cleaning techniques (handling missing data, outliers, duplicates)
- Exploratory Data Analysis (EDA) using Python and R
- Feature engineering and dimensionality reduction
- Python for Data Science (NumPy, Pandas, Matplotlib, Seaborn)
- SQL for Data Analysis (queries, joins, aggregations)
- R-Programming for statistical computing and visualization
- Introduction to Jupyter Notebooks and Google Colab
- Descriptive vs. inferential statistics
- Hypothesis testing and confidence intervals
- Probability distributions (normal, binomial, Poisson)
- Correlation, covariance, and regression analysis
- Supervised vs. unsupervised learning
- Training, testing, and model evaluation techniques
- Regression algorithms (Linear Regression, Logistic Regression)
- Classification algorithms (Decision Trees, Random Forest, SVM, KNN)
- Clustering techniques (K-Means, Hierarchical Clustering)
- Deep Learning concepts and Neural Networks
- Hands-on with TensorFlow and PyTorch
- Natural Language Processing (NLP) and sentiment analysis
- Image recognition and computer vision applications
- Model optimization techniques (Grid Search, Hyperparameter Tuning)
- Creating interactive dashboards with Tableau and Power BI
- Data storytelling and effective visualization techniques
- Using Matplotlib and Seaborn for Python-based visualization
- Real-world case studies on data-driven decision-making
- Introduction to Big Data technologies (Hadoop, Spark)
- Cloud computing platforms: AWS, Azure, and Google Cloud
- Deploying machine learning models on cloud platforms
- Working with databases and data lakes for large-scale analysis
- End-to-end data science project with real-world datasets
- Building and deploying a machine learning model
- Presentation of insights and storytelling with data
- Capstone project: Solving an industry-specific problem
- Preparation for globally recognized certifications (AWS Data Analytics, TensorFlow Developer, Microsoft Certified Data Scientist)
- Resume building and interview preparation
- Networking strategies and job market insights
- Freelancing and entrepreneurship in data science
Why Enroll in This Program?
- Gain hands-on experience with top data science tools and frameworks.
- Work on real-world projects and build a strong portfolio.
- Prepare for global certifications to boost your career.
- Learn from industry experts and receive career mentorship.
- Stay ahead in the rapidly evolving AI and data-driven world.
Start your journey to becoming a globally certified data scientist today!