Specialization: Overview Business Analytics
Specialization: Business Analytics
Course 1. Advanced Excel
- Features: Includes data analysis tools like PivotTables, PivotCharts, VLOOKUP, INDEX-MATCH, and complex formulas. Offers data visualization with conditional formatting, charts, and sparklines. Also covers data cleaning, what-if analysis, and automation with macros.
- Not: A full-scale database or substitute for dedicated BI tools; limited in handling large data sets efficiently.
Course 2. MS Access
- Features: Provides a user-friendly interface for database management. Allows the creation of tables, queries, forms, and reports. Useful for small-to-medium databases and supports SQL for advanced querying.
- Not: Suitable for enterprise-level data management or very large datasets; lacks the performance of industrial database systems like SQL Server or Oracle.
Course 3. Power BI
- Features: A business analytics tool for creating interactive data visualizations and reports. Allows data integration from multiple sources, custom dashboards, DAX formulas for calculations, and easy sharing of insights.
- Not: A tool for direct data manipulation or detailed data cleaning; lacks the spreadsheet-like interface of Excel for granular data handling.
Course 4. Python
- Features: A versatile programming language with powerful libraries for data analysis (Pandas, NumPy), visualization (Matplotlib, Seaborn), and machine learning (scikit-learn). Ideal for automation, large-scale data manipulation, and advanced analytics.
- Not: A traditional BI tool; lacks built-in visualization and report-sharing features typical of BI platforms.
Course 5. SQL (Structured Query Language)
- Features: A standardized language for managing and querying relational databases. Supports data retrieval, updating, insertion, deletion, and complex joins and aggregations.
- Not: Designed for data visualization or predictive analytics; requires integration with other tools (like Power BI) for a full analytics pipeline.
Overview of Tools for Data Analytics and Management
| Tool | Strengths | Limitations | Ideal Use Cases |
|---|---|---|---|
| Advanced Excel | PivotTables, Formulas, Conditional Formatting | Limited Data Size, Basic Visualization, Paid | Quick Analysis, Budget-Friendly |
| MS Access | Relational DB, Forms, Reports | Limited Scalability, Basic SQL, Local Only, Paid | Small Business Databases, Data Storage |
| Power BI | Visualization, DAX, Multi-source Integration | Limited Data Manipulation, Costly for Teams, Paid | Real-time Reporting, Dashboards |
| Python | Data Analysis Libraries, Automation, Custom | Coding Required, Limited BI Reporting, Open Source | Data Engineering, ML, Large Datasets |
| SQL | Data Querying, Filtering, Optimized Joins | No Visualization, Limited Data Manipulation | Relational DB Management, Complex Queries |
Key Data Disciplines: Definitions, Skills, and Career Paths
| Discipline | Definition | Key Skills | Prerequisites | Career Path |
|---|---|---|---|---|
| Data Science | Combines statistics, mathematics, programming, and domain expertise to extract insights and make predictions from complex data. | Hard Skills: Statistics, Python/R, machine learning, SQL, data wrangling, visualization. Soft Skills: Problem-solving, critical thinking, communication. |
Strong foundation in statistics, probability, linear algebra, calculus; programming proficiency in Python, R, SQL. | Data Scientist, Machine Learning Engineer, AI Specialist |
| Business Analysis | Focuses on understanding business needs, defining requirements, and designing solutions to improve processes and decision-making. | Hard Skills: Requirements gathering, visualization, basic statistics, Excel, SQL, ERP/CRM systems. Soft Skills: Communication, stakeholder management, negotiation, critical thinking. |
Understanding of business processes, project management fundamentals, basic data analysis tools. | Business Analyst, Product Analyst, Project Manager |
| Big Data | Manages and processes large, complex datasets that traditional data tools cannot handle. | Hard Skills: Hadoop, Spark, NoSQL, data warehousing, cloud platforms, Java, Python, Scala. Soft Skills: Problem-solving, critical thinking, adaptability. |
Basic knowledge of databases, distributed and cloud computing; programming in Python or Java. | Big Data Engineer, Data Architect, Data Engineer |
| Data Analytics | Involves examining datasets to draw conclusions, often for business decision-making and process improvements. | Hard Skills: Statistics, Excel, SQL, Power BI/Tableau, basic programming. Soft Skills: Communication, analytical thinking, attention to detail. |
Basic understanding of statistics, data visualization, databases; familiarity with business processes and KPIs. | Data Analyst, Marketing Analyst, Business Intelligence Analyst |
Data Skills Progression Across Disciplines: A Bloom’s Taxonomy Approach
| Bloom’s Level | Skill | Discipline |
|---|---|---|
| Remembering | Basic statistics, definitions, data types, SQL fundamentals | All disciplines |
| Understanding | Interpreting data, visualizing data, business context, data structures | Data Analytics, Business Analysis |
| Applying | Implementing data models, using BI tools, basic coding, running queries | Applied Data Analysis, Data Science |
| Analyzing | Hypothesis testing, statistical analysis, data transformation, machine learning | Data Science, Data Analytics |
| Evaluating | Model evaluation, validation, stakeholder reporting, data quality assessment | Data Science, Big Data, ML/AI |
| Creating | Building models, data pipelines, infrastructure, designing data-driven solutions | Data Engineering, Machine Learning |
Essential Goals and Mindsets for Future Data Leaders
1. Vision: The Power of Data to Transform “Data-driven decisions can shape the future, whether in business, healthcare, or social change. We’re not just learning skills; we’re gaining tools to interpret the world and drive meaningful impact.”
2. Mission: Building Analytical and Ethical Data Experts “Our mission is to develop skilled analysts and scientists who are not only technically proficient but also understand the ethical responsibility of working with data.”
3. Goal: Mastering Data Fundamentals First “Start by building a strong foundation in core data principles—statistics, programming, and understanding business contexts. Mastering these basics will make advanced topics like machine learning, big data, and analytics much easier to grasp.”.
4. Principle: Learning by Doing “The best way to learn is by applying what you know to real-world problems. Practicing on real datasets and creating hands-on projects builds confidence and practical skills.”
5. Focus: Problem-Solving Over Tool-Knowledge “Tools will change over time, but analytical thinking and problem-solving are skills for life. Focus on understanding how to ask the right questions, analyze data, and derive insights, no matter what tools you’re using.”
6. Goal: Be Curious, Be Adaptable “Stay curious and keep exploring new trends and innovations in data. Adaptability is key, as data fields evolve rapidly—today’s tools and methods may look very different tomorrow.”
7. Reminder: Build Communication and Storytelling Skills “Data is only powerful when its story is well told. Learn to communicate your findings effectively and make complex insights accessible to all audiences.”
Python Ecosystem: The Powerhouse for Data Science and Analytics
| Package | Purpose | Features | Use Cases |
|---|---|---|---|
| NumPy | Numerical computing | Multi-dimensional arrays, mathematical functions | Data manipulation, linear algebra, random generation |
| Pandas | Data manipulation and analysis | DataFrames, data cleaning, filtering, merging, aggregation | Data wrangling, preprocessing, exploratory analysis |
| Matplotlib | Basic data visualization | Static, animated, interactive visualizations like line charts, histograms, scatter plots | Data visualization, exploratory analysis |
| Seaborn | Statistical data visualization | Simplified statistical plots, distribution, categorical plots | Visualizing distributions, relationships |
| SciPy | Scientific computing | Optimization, integration, interpolation, eigenvalue problems | Scientific analysis, signal processing |
| Scikit-Learn | Machine learning | Classification, regression, clustering, dimensionality reduction, model selection | Machine learning models, preprocessing, model evaluation |
| Statsmodels | Statistical modeling | Statistical models, regression, time series analysis | Regression analysis, hypothesis testing, econometrics |
| TensorFlow | Deep learning | Neural networks, model building, training, deployment | Image recognition, NLP, advanced AI applications |
| PyTorch | Deep learning and research | Flexible neural networks, dynamic computation graphs | Academic research, prototyping neural networks |
| Keras | High-level neural networks API | Simplifies neural network building and training (often with TensorFlow) | Rapid deep learning prototyping |
| NLTK | Natural language processing | Text processing, tokenization, named entity recognition | Text mining, sentiment analysis |
| spaCy | Advanced NLP | Optimized NLP pipelines, dependency parsing, named entity recognition | Production-level NLP, information extraction |
| BeautifulSoup | Web scraping | HTML and XML parsing | Web data collection, simple web scraping tasks |
| Scrapy | Web scraping framework | Comprehensive web crawling and scraping functionality | Automated data collection, large-scale web scraping |
| SQLAlchemy | Database manipulation | ORM for database connections and SQL operations | SQL querying, relational data storage |
| Dask | Parallel computing | Scales computations across multiple cores/clusters, compatible with NumPy and Pandas | Handling large datasets, distributed computing |
| XGBoost | Gradient boosting for ML | Efficient, high-performance gradient boosting | High-accuracy models for structured data |
| LightGBM | Gradient boosting for ML | Fast and memory-efficient gradient boosting | Competitions, structured data prediction |
| Plotly | Interactive data visualization | Interactive and web-based visualizations for dashboards | Interactive dashboards, advanced visualizations |
| Bokeh | Interactive data visualization | Interactive visualizations suitable for web applications | Web-based data presentations, real-time visualizations |
Introduction to Your Course Facilitator (Python): Building Knowledge Together
Mohsin Yaseen
- Degree and Discipline: MSc. Computer Science (2006)
- Profession: ERP Business Analyst (Data, Process, Compliance, Solution Evaluation, Stakeholder Needs)
- Current Engagement: Principal Manager in KICS UET Lahore and CEO SolBizTech
- Certification in: PMP, Project Management By Google, CHRP, CSCP, MS Power BI, Odoo (ERP), Salesforce
- Professional Experience: 17 years in ERP Business Analyst, Project Management, Product Development, Software Development
- Profile: https://www.linkedin.com/in/rmyasin/
We will discuss 'DATA' in detail in start of Second Week.
Course Contents - Python in Data Anlytics
Week 01-01: Introduction to Python for Business Analytics
Objective: Familiarize participants with Python basics, key libraries, and its use in business analytics.
- Introduction to Python and the Jupyter Notebook
- Basic syntax, data types (lists, tuples, dictionaries), and functions
- Overview of Python libraries for business analytics: Pandas, NumPy, Matplotlib, Seaborn
- Introduction to data structures for handling business data (Pandas DataFrames)
- Hands-on exercise: Setting up the environment and writing basic Python code
- Mini-project: Analyzing a sample business dataset (e.g., sales or customer data)
Week 02: Data Manipulation and Cleaning with Pandas
Objective: Teach participants to clean and prepare data for analysis, handling common issues in business datasets.
- Importing and exporting data (CSV, Excel, JSON)
- Data manipulation with Pandas: selecting, filtering, merging, and aggregating data
- Data cleaning: handling missing values, duplicate data, outliers, and data types
- Exploratory Data Analysis (EDA) techniques for business insights
- Hands-on exercise: Cleaning and preparing a business dataset
- Mini-project: Using Pandas to prepare a dataset for analysis (e.g., sales or financial data)
Week 03: Overview of Python NumPy
Objective: Introduce the essential functionality of NumPy for efficient numerical computations in Python. Cover the creation, manipulation, and analysis of arrays, along with basic statistical operations relevant for data science and engineering applications.
- Introduction to NumPy and Creating and Manipulating Arrays Overview of NumPy’s role in Python for efficient numerical and scientific computing. Discuss its benefits for handling large datasets and performing complex calculations. Learn to create arrays from lists, ranges, and functions like np.zeros and np.ones. Covers reshaping, changing dimensions, and understanding array structures.
- Array Operations and Math Functions and Indexing, Slicing, and Filtering Perform element-wise operations such as addition and multiplication. Explore aggregate functions like np.sum and np.mean for data summarization. Access specific elements or subsets using indexing and slicing. Apply Boolean indexing to filter data based on conditions.
- Random Number Generation and Statistical Analysis with NumPy Generate random numbers and arrays for simulations using np.random functions. Useful for creating test data and sampling. Use statistical functions to calculate mean, median, variance, and percentiles. Analyze data distributions and trends within datasets.
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