Data Science Training and Certification in Uyo, Akwa Ibom State and Port-Harcourt, Rivers State

Data Science Training and Certification in Uyo, Akwa Ibom State and Port-Harcourt, Rivers State

DATA SCIENCE TRAINING COURSE

 (Analysis, Visualization, Prediction, Machine Learning, and Deep Learning)

Go from Beginner to Pro.

 

Data Science Training Course Outline:

This comprehensive course outline covers the essential concepts and tools for data science, including analysis, visualization, prediction, machine learning, and deep learning.

 

Section 1: Introduction to Data Science 

Module 1: What is Data Science?

Module 2: The Data Science Lifecycle

Module 3: Data Acquisition and Exploration

Module 4: Data Cleaning and Preprocessing

Module 5: Introduction to Statistics and Probability

Module 6: Introduction to Programming for Data Science (Python)

Module 7: Version Control with Git

Module 8: Data Science Ethics and Responsible AI

 

Section 2: Data Analysis and Visualization 

Module 9: Introduction to Data Visualization Principles

Module 10: Storytelling with Data

Module 11: Data Visualization with Excel

Module 12: Data Visualization with Power BI

Module 13: Data Visualization with Tableau

Module 14: Advanced Visualization Libraries in Python (Matplotlib, Seaborn)

Module 15: Interactive Data Visualization with Plotly and Bokeh

Module 16: Data Wrangling with Pandas

Module 17: Data Aggregation and Grouping

Module 18: Exploratory Data Analysis (EDA) Techniques

Module 19: Descriptive Statistics

Module 20: Inferential Statistics (Hypothesis Testing)

Module 21: Time Series Analysis with Pandas

Module 22: Geospatial Data Analysis with Libraries like GeoPandas

Module 23: Text Analysis with Natural Language Processing (NLP)

Module 24: Social Network Analysis

 

Section 3: Predictive Modeling 

Module 25: Introduction to Predictive Modeling

Module 26: Linear Regression

Module 27: Logistic Regression

Module 28: K-Nearest Neighbors (KNN)

Module 29: Decision Trees and Random Forests

Module 30: Support Vector Machines (SVM)

Module 31: Ensemble Methods (Boosting, Bagging)

Module 32: Model Evaluation Metrics

Module 33: Model Selection and Tuning

Module 34: Feature Engineering for Machine Learning

Module 35: Dimensionality Reduction Techniques (PCA, LDA)

Module 36: Time Series Forecasting with ARIMA and Prophet

Module 37: Survival Analysis with Kaplan-Meier Curves and Cox Proportional Hazards Model

Module 38: Recommender Systems

Module 39: Introduction to Deep Learning

Module 40: Deep Learning Architectures (CNN, RNN, LSTM)

 

Section 4: Machine Learning with Python Libraries 

Module 41: Scikit-Learn for Machine Learning

Module 42: XGBoost and LightGBM for Gradient Boosting

Module 43: CatBoost for Gradient Boosting

Module 44: TensorFlow Fundamentals

Module 45: Keras for Deep Learning

Module 46: PyTorch for Deep Learning

Module 47: Theano 

Module 48: Caffe

Module 49: Deep Learning with MXNet 

Module 50: Natural Language Processing with Libraries like NLTK, spaCy

Module 51: Computer Vision with Libraries like OpenCV

Module 52: Time Series Forecasting with Libraries like Prophet

Module 53: Survival Analysis with Libraries like lifelines

Module 54: Recommender Systems with Libraries like Surprise

Module 55: Unsupervised Learning Techniques (Clustering, PCA)

Module 56: Reinforcement Learning with Libraries like OpenAI Gym

 

Section 5: Data Management and Databases

Module 57: Introduction to Database Management Systems (DBMS)

Module 58: SQL for Data Retrieval and Manipulation

Module 59: Introduction to NoSQL Databases (MongoDB, Apache Cassandra)

Module 60: Big Data Concepts and Technologies

Module 61: Cloud Computing for Data Science (AWS, Azure, GCP) (Optional)

Module 62: Data Warehousing and Business

 

Target Audience: This course is designed for individuals with an interest in data science, including:

 

• Business analysts
• Data analysts
• Researchers
• Programmers
• Anyone who wants to leverage data for insights and decision making