Description
Global Certification Program
- E-Learning
- Globally lifetime Valid Certification
- Lifetime Valid Certification
- AI based Roleplay & Simulations
- Two Exam Attempts
Certified in Data Science Professional
The GSDC Certified Data Science Professional certification is a well-known and highly desired certification for data science professionals. It proves that individuals have the necessary skills and expertise in data science. Data Science certification is for people who already know a lot about data science and want to show they can apply their knowledge in real-world situations.
Getting certified by GSDC shows that a person is committed to keeping up with the latest developments in the field and can handle difficult data challenges. It also helps them stand out and advance their career in the fast-changing world of data science.
Learning Objectives
❑ Showcase practical application of data science skills.
❑ Enhance credibility as a data science professional.
❑ Demonstrate ability to analyze and interpret data.
❑ Validate competence in data-driven decision-making.
❑ Boost confidence in handling complex data projects.
❑ Stay updated with evolving data science methodologies.
❑ Increase marketability and job prospects in data science.
❑ Establish oneself as a certified data science practitioner.
❑ Join a distinguished community of data science professionals.
Curriculum
1.Data Science Introduction:
• Overview of Data Science: Definition, Importance, and Applications
• Key Concepts and Terminology in Data Science
• The Data Science Lifecycle: Problem Definition, Data Collection, Data Preparation, Data Analysis, and Interpretation
• Tools and Technologies in Data Science: Python, R, SQL, etc.
• Introduction to Data Science Roles: Data Scientist, Data Analyst, Data Engineer, and their Responsibilities
2.Probability and Statistics for Data Science:
• Basic Probability Concepts: Probability Theory, Random Variables, Probability Distributions
• Statistical Methods: Descriptive Statistics, Inferential Statistics, Hypothesis Testing
• Correlation and Regression Analysis
• Bayesian Statistics
• Importance of Probability and Statistics in Data Science
3.Working with Big Data:
• Introduction to Big Data: Definition, Characteristics, and Challenges
• Big Data Technologies: Hadoop, Spark, NoSQL Databases
• Data Storage Solutions: HDFS, Cassandra, MongoDB
• Big Data Processing Frameworks: MapReduce, Spark
• Case Studies and Applications of Big Data
4.Data Extraction and Visualization Techniques:
• Data Extraction Techniques: Web Scraping, APIs, Databases
• Data Cleaning and Preparation: Handling Missing Values, Data Transformation, Data Integration
• Introduction to Data Visualization: Importance, Principles, and Tools
• Visualization Techniques: Charts, Graphs, Plots, Dashboards
• Tools for Data Visualization: Matplotlib, Seaborn, Plotly
5.Data Mining and Machine Learning:
• Fundamentals of Data Mining: Concepts, Techniques, and Applications
• Machine Learning Overview: Supervised, Unsupervised, and Reinforcement Learning
• Key Algorithms: Regression, Classification, Clustering, Association
• Model Evaluation and Validation Techniques
• Practical Applications of Data Mining and Machine Learning
6.A Statistical Approach for Data Science:
• Advanced Statistical Methods: Multivariate Analysis, Time Series Analysis, Survival Analysis
• Statistical Inference: Estimation, Confidence Intervals, Significance Testing
• Experimental Design and Analysis
• Statistical Software: R, Python (stats models, SciPy)
• Real-world Applications of Statistical Methods in Data Science
7.Text Mining & NLP:
• Introduction to Text Mining: Techniques, Applications, and Challenges
• NLP Fundamentals: Text Preprocessing, Tokenization, Stemming, Lemmatization
• Text Classification, Sentiment Analysis, and Topic Modeling
• Advanced NLP Techniques: Word Embeddings, Transformers, BERT, GPT
• Tools for Text Mining and NLP: NLTK, SpaCy, Gensim
8.Data Visualization Using Tableau:
• Introduction to Tableau: Overview and Installation
• Connecting to Data Sources in Tableau
• Creating Basic Visualizations: Bar Charts, Line Charts, Pie Charts
• Advanced Visualizations: Heat Maps, Tree Maps, Scatter Plots
• Building Interactive Dashboards and Storytelling with Data
• Deep Dive into Tableau Features and Functionalities
• Advanced Data Manipulation and Calculations in Tableau
• Using Tableau for Exploratory Data Analysis
• Integrating Tableau with Other Data Science Tools
• Best Practices for Effective Data Visualization and Presentation