Our comprehensive Data Science Certification Course is designed to equip you with the essential skills and knowledge to excel in the rapidly evolving field of data science. Covering key areas such as machine learning, statistical analysis, data visualization, and big data technologies, this course provides a perfect blend of theoretical foundation and practical application. Whether you’re a beginner or a professional looking to enhance your expertise, our expert-led training, hands-on projects, and flexible learning options will prepare you for a successful career in data science.
₹9,999
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Discover the power of data science certification and gain skills in advanced Excel, Python, Tableau, and SQL, providing you with a comprehensive toolkit for success.Our course ensures you are proficient in the latest industry-standard tools and methodologies, enabling you to derive actionable insights from complex datasets.
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Embark on a transformative educational journey with edxprt, your portal to global study opportunities, professional growth, and unparalleled learning experiences.Data Science Certification course equips data professionals with deep conceptual understanding of and hands-on training in all the advanced tools and techniques needed to make data-driven business decisions. Experience immersive live online learning to gain actionable insights through a mix of lectures, tutorials using data, hands-on experience with tools like Python, and real-world case study discussions. Data Science Course provides you with detailed learning in data science, data analytics, project life cycle, data acquisition, analysis, statistical methods and machine learning. The core course modules have been designed to suit different job profiles – data administrators, business analysts, data analysts, data engineers, data architects and data scientists. We cover advanced machine learning algorithms that most other courses don’t! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.
Our curriculum is designed to provide a balanced mix of theoretical knowledge and practical application for data science certification:
Understand the significance of data science, its lifecycle, and the roles within the field. Get acquainted with the essential tools and technologies used by data scientists.
- Overview of Data Science
- Definition and significance of data science
- The data science lifecycle
- Key roles and responsibilities of a data scientist
- Tools and Technologies
- Introduction to Python and R
- Overview of data science libraries and frameworks (Pandas, NumPy, SciPy, scikit-learn)
- Setting up the data science environment
Learn to import, export, clean, and preprocess data using various techniques. Gain proficiency in handling different data formats and preparing data for analysis.
- Data Importing and Exporting
- Reading and writing data in various formats (CSV, Excel, JSON, SQL)
- Connecting to databases and APIs
- Data Cleaning and Preprocessing
- Handling missing values
- Data transformation and normalization
- Dealing with outliers
- Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and correlations
Build a strong foundation in statistical concepts, including probability, distributions, and hypothesis testing. Apply these concepts to infer meaningful insights from data.
- Fundamentals of Statistics
- Probability theory
- Distributions (Normal, Binomial, Poisson)
- Hypothesis testing
- Inferential Statistics
- Confidence intervals
- T-tests and ANOVA
- Chi-square tests
Master the art of data visualization using tools like Matplotlib, Seaborn, and Tableau. Create effective visualizations that communicate your findings clearly and concisely.
- Introduction to Data Visualization
- Importance and principles of effective visualization
- Types of visualizations (bar charts, histograms, scatter plots, etc.)
- Tools for Data Visualization
- Matplotlib and Seaborn in Python
- ggplot2 in R
- Advanced visualizations with Tableau
Dive into machine learning algorithms, including supervised and unsupervised learning. Learn to build, evaluate, and deploy models to solve real-world problems.
- Introduction to Machine Learning
- Types of machine learning (supervised, unsupervised, reinforcement learning)
- The machine learning workflow
- Supervised Learning Algorithms
- Linear and logistic regression
- Decision trees and random forests
- Support vector machines (SVM)
- Unsupervised Learning Algorithms
- K-means clustering
- Principal component analysis (PCA)
- Anomaly detection
- Model Evaluation and Validation
- Cross-validation techniques
- Performance metrics (accuracy, precision, recall, F1 score)
Explore advanced topics such as ensemble methods, neural networks, and natural language processing. Gain deeper insights into complex machine learning techniques.
- Ensemble Methods
- Bagging and boosting
- Gradient boosting machines (GBM)
- XGBoost
- Neural Networks and Deep Learning
- Introduction to neural networks
- Convolutional neural networks (CNN)
- Recurrent neural networks (RNN)
- Natural Language Processing (NLP)
- Text preprocessing techniques
- Sentiment analysis
- Topic modeling
Get introduced to big data concepts and technologies like Hadoop and Spark. Learn to process and analyze large datasets efficiently using these powerful tools.
- Introduction to Big Data
- Characteristics and challenges of big data
- Overview of Hadoop and Spark
- Data Processing with Spark
- Introduction to Spark architecture
- Working with RDDs and DataFrames
- Spark SQL and MLib
Apply your skills to a comprehensive capstone project that simulates a real-world data science challenge. This project will be a key part of your professional portfolio.
- Project Planning and Execution
- Defining project objectives and scope
- Data collection and preprocessing
- Model building and evaluation
- Reporting and presenting results
Prepare for your career with dedicated modules on building a portfolio, writing an effective resume, and preparing for interviews. Benefit from mock interviews and feedback sessions.
- Building a Data Science Portfolio
- Showcasing projects and skills
- Writing an effective resume and cover letter
- Interview Preparation
- Common interview questions and answers
- Technical and behavioral interview tips
- Mock interviews and feedback
Review key concepts and practice with mock exams to ensure you are fully prepared for the certification exam. Learn strategies for effective exam preparation and time management.
- Review of Key Concepts
- Comprehensive review of all modules
- Practice tests and quizzes
- Exam Strategy
- Tips for effective exam preparation
- Time management during the exam
Supplementary Reading Materials: Access a curated list of textbooks, research papers, and online resources to deepen your understanding.
Community and Support: Join discussion forums, study groups, and regular Q&A sessions with instructors to stay connected and supported throughout your learning journey.
₹19,900
Become a Data Scientist!
Gain proficiency in the essential tools and techniques needed to excel as a Data Scientist and stand out in the competitive job market.FAQs on Data Science Certification
Master Data Science and Elevate Career!
Equip yourself with the most sought-after data science skills. Our hands-on projects and real-world scenarios ensure you are job-ready and capable of tackling complex data challenges.Achieve Excellence with a Certificate!
A certificate in data science is a formal recognition of your skills and knowledge in data analysis, machine learning, and data-driven decision making
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I was impressed by the depth and quality of the edxprt Data Analyst Certification course. The instructors are knowledgeable and always willing to help. The skills I gained have been directly applicable to my role, leading to improved performance and recognition at work.
