Become a Data Analyst
New Year Special
MS Excel | Power BI | SQL | Python
Add-Ons: Tableau | R | Cloud Computing | Upwork
Become a Data Analyst
MS Excel | Power BI | SQL | Python
Join our New Year "Become a Data Analyst" course and master MS Excel, Power BI, SQL, and Python.
Through these 66 hours of classes, you'll gain the skills necessary to thrive in the competitive job market or launch a successful freelance career. Unleash your potential as a data analyst!
Module 1: Excel for Data Analysis
Data Analysis
- Excel Tips & Tricks Save Time
- Working with Organize and Un-Organize Data
- Analyzing & Extracting Data with Filters & Advance Filter
- Data Validation Techniques
- Working with Custom Formatting
- Working with Multiple Worksheets and workbooks (Links)
- Applying Security to Files, Workbooks & Worksheets
- Subtotals and outlining for data summarization
Data Analysis Techniques using Pivot Table
- Understanding Database Management and utilization of Pivot Tables to manage Data
- Creating reports with PivotTable
- Summarizing data month-wise, Qtr wise & year wise with grouping and aggregation
- Grouping Numeric & Date fields in Pivot Table
- Analyzing Sales Trends, Growth, Variance
- Using slicers and timelines for interactive filtering
Advanced Excel Functions and Techniques
- Lookup and Reference Functions
- Nested Functions and Formula Auditing
- Array Formulas and Advanced Lookup Functions
- Statistical Functions for Data Analysis
- Data Consolidating Techniques
- Working with the Name Manager
- Logical Functions (IF, AND, OR)
What-if-Analysis
- Data Trend
- Goal Seek
- Forecast techniques
Data Analysis using Pivot Tables
- Understanding Pivot Tables and their Benefits
- Creating Pivot Tables from Data Ranges
- Applying Filters and Slicers to Pivot Tables
- Advanced Pivot Table Techniques
- Grouping and Subtotaling Data in Pivot Tables
- Calculated Fields and Items in Pivot Tables
- Using Pivot Table Options for Customization
- Creating Pivot Charts from Pivot Tables
- Customizing Pivot Charts (Styles, Types, Formatting)
Understanding Dashboard Design Best Practices
- Integrating Pivot Tables and Pivot Charts into Dashboards
- Data Visualization Principles
- Designing Interactive Dashboards with Excel
- Creating Sparklines for In-Line Data Trends
- Working with Slicers + Sparklines
Module 2: Power BI for Data Analysis
Introduction to Power BI
- Overview of Power BI
- Understanding the Power BI ecosystem
- Installation and setup
- Getting familiar with Power BI Desktop and Power BI Service
Data Transformation and Cleansing
- Cleaning and transforming data using Power Query Editor
- Handling missing or inconsistent data
- Merging and appending queries
- Advanced data transformation techniques
Connecting to Data Sources
- Importing data from various sources (Excel, CSV, databases, web sources, etc.)
- Understanding data shaping and transformation
- Data modeling best practices
Data Modeling
- Introduction to data modeling concepts
- Creating relationships between tables
- Using DAX (Data Analysis Expressions) for calculations and measures
- Introduction to calculated columns and measures
Visualization Techniques
- Creating different types of visualizations (bar charts, line charts, pie charts, etc.)
- Customizing visualizations for better insights
- Using slicers, filters, and drill-downs for interactivity
- Best practices for effective visualizations
Advanced Analytics with Power BI
- Introduction to advanced analytics features (forecasting, clustering, etc.)
- Implementing time intelligence functions in DAX
Power BI Service and Sharing
- Publishing reports and dashboards to Power BI Service
- Configuring dashboards and sharing options
- Collaboration and collaboration features
- Managing security and permissions
Module 3: SQL for Data Analysis
Introduction to SQL Server
- Overview of SQL Server
- What is SQL Server?
- Installation and Setup
- SQL Server Management Studio (SSMS) Interface
Basics of SQL
- Introduction to SQL
- SQL Syntax and Structure
- Data Types
- Basic SQL Commands
- SELECT, FROM, WHERE
- INSERT, UPDATE, DELETE
- Filtering and Sorting Data
- WHERE Clause
- ORDER BY Clause
Advanced SQL Queries
- Aggregate Functions
- COUNT, SUM, AVG, MIN, MAX
- Grouping Data
- GROUP BY Clause
- HAVING Clause
- Joining Tables
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
Data Manipulation and Transformation
- Subqueries and Nested Queries
- Common Table Expressions (CTEs)
- Window Functions
- ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
- Data Transformation Techniques
Working with Complex Data Types
- Working with Dates and Times
- Date Functions
- String Functions
- CONCAT, SUBSTRING, CHARINDEX, REPLACE
SQL Server Advanced Topics
- Indexing and Performance Tuning
- Creating and Managing Indexes
- Query Optimization
- Stored Procedures and Functions
- Creating and Executing Stored Procedures
- User-Defined Functions
Data Analysis and Reporting
- Basic Data Analysis Techniques
- Descriptive Statistics
- Using SQL for Data Analysis
- Exploratory Data Analysis (EDA)
- Generating Reports
- Creating Simple Reports in SSMS
- Exporting Data to Excel
Hands-on Projects and Case Studies
- Real-world Data Analysis Projects
- Case Studies
Module 4: Python for Data Analysis
Introduction to Python for Data Analysis
- Overview of Python and its Applications in Data Analysis
- Why Python for Data Analysis?
- Setting Up Python Environment
- Introduction to Jupyter Notebooks
Python Basics
- Python Syntax and Basics
- Variables, Data Types, and Operators
- Control Structures: if-else, loops
- Functions and Modules
- Defining and Calling Functions
- Importing and Using Modules
Data Structures and Handling
- Working with Lists, Tuples, and Dictionaries
- Introduction to NumPy
- Creating and Manipulating Arrays
- Array Operations and Broadcasting
Data Manipulation with Pandas
- Introduction to Pandas
- DataFrames and Series
- Data Loading, Cleaning, and Preparation
- Reading and Writing Data (CSV, Excel, SQL, etc.)
- Handling Missing Data
- Data Transformation and Filtering
Data Analysis and Exploration
- Exploratory Data Analysis (EDA)
- Descriptive Statistics
- Data Visualization with Matplotlib and Seaborn
- Plotting and Customizing Graphs
- Grouping and Aggregating Data
- groupby() and aggregate functions
Working with Dates and Times
- Date and Time Data Types and Operations
- Resampling and Time Series Analysis
- Date Functionality in Pandas
Advanced Data Manipulation Techniques
- Merging and Joining DataFrames
- concat(), merge(), join() Methods
- Pivot Tables and Crosstabulations
- Creating and Manipulating Pivot Tables
Practical Applications and Case Studies
- Real-world Data Analysis Projects
- Case Study: Analyzing a Dataset (e.g., Titanic Dataset)
- Creating Reports and Dashboards
- Integration with Other Tools
- Exporting Results to Excel and Other Formats
Add-Ons: Tableau for Data Analysis
Introduction to Tableau
- Overview of Tableau and its uses in data analysis.
- Understanding the Tableau interface.
- Connecting to data sources.
Data Preparation
- Data sourcing and importing.
Visualization Basics
- Creating basic visualizations (bar charts, line charts, pie charts, etc.).
- Applying filters and sorting data.
- Formatting visualizations for clarity.
Advanced Visualization Techniques
- Creating interactive dashboards.
- Using parameters and calculated fields.
- Implementing advanced chart types (treemaps, heatmaps, etc.).
Data Analysis
- Exploring trends and patterns in data.
- Conducting ad-hoc analysis with Tableau.
- Utilizing forecasting and trend analysis tools.
Mapping Data
- Geospatial analysis with maps.
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- Analyzing location-based data.
Sharing and Collaboration
- Publishing workbooks to Tableau Public.
- Sharing insights and visualizations with stakeholders.
- Collaborating on projects within Tableau.
Add-Ons: R Language for Data Analysis
Introduction to R and RStudio
- Overview of R Language
- What is R and its Applications in Data Analytics?
- Installing R and RStudio
- Introduction to the RStudio Interface
- Basic R Syntax
- Variables and Data Types
- Basic Operations
Data Structures in R
- Vectors, Matrices, Lists, and Data Frames
- Creating and Manipulating Vectors
- Understanding Matrices
- Working with Lists
- Introduction to Data Frames
Data Manipulation with dplyr
- Introduction to the dplyr Package
- Installing and Loading dplyr
- Basic Data Manipulation
- Filtering Rows with filter()
- Selecting Columns with select()
- Arranging Rows with arrange()
- Mutating Data with mutate()
- Summarizing Data with summarize()
Basic Data Visualization with ggplot2
- Introduction to ggplot2
- Installing and Loading ggplot2
- Creating Basic Plots
- Scatter Plots, Line Plots, Bar Plots
- Customizing Plots
- Adding Titles, Labels, and Themes
Add-Ons: Introduction to Cloud Computing for Data Analysts
Overview of Cloud Computing
- Definition and Key Concepts of Cloud Computing
- What is Cloud Computing?
- Key Characteristics (On-Demand Self-Service, Broad Network Access, Resource Pooling, Rapid Elasticity, Measured Service)
- Types of Cloud Computing
- Public, Private, and Hybrid Clouds
- Cloud Service Models
- Infrastructure as a Service (IaaS)
- Platform as a Service (PaaS)
- Software as a Service (SaaS)
Cloud Computing for Data Analysis
- Benefits of Using Cloud for Data Analysis
- Scalability, Flexibility, Cost Efficiency
- Introduction to Cloud Storage Solutions
- Object Storage (e.g., Amazon S3, Azure Blob Storage, Google Cloud Storage)
- File Storage and Database Storage Options
- Data Warehousing in the Cloud
- Overview of Cloud Data Warehouses (e.g., Amazon Redshift, Google BigQuery, Azure Synapse Analytics)
Key Cloud Services for Data Analysts
- Introduction to Cloud Data Processing Tools
- Big Data Processing (e.g., AWS EMR, Azure HDInsight, Google Dataflow)
- Serverless Computing (e.g., AWS Lambda, Azure Functions, Google Cloud Functions)
- Cloud-Based SQL Databases
- Managed Database Services (e.g., Amazon RDS, Azure SQL Database, Google Cloud SQL)
- Introduction to Data Integration and ETL Tools in the Cloud
- ETL Services (e.g., AWS Glue, Azure Data Factory, Google Cloud Dataflow)
Practical Applications and Case Studies
- Real-World Examples of Cloud Data Analysis
- Case Study: Using Cloud Storage for Data Analysis
- Case Study: Data Warehousing and Big Data Processing
- Demonstration of Cloud Tools
- Brief Demo of a Cloud Storage Solution
- Brief Demo of a Cloud-Based Data Warehouse
Add-Ons: Start Freelancing on Upwork
Introduction to Upwork
What is Upwork?
Benefits of being an Upwork freelancer
Overview of the Upwork platform
Setting Up Your Upwork Profile
Creating an effective profile
Crafting a compelling title and overview
Highlighting your skills and expertise
Building a portfolio and showcasing your work
Finding and Applying for Jobs
Search strategies and filters
Understanding job descriptions and requirements
Crafting winning proposals
Following up on proposals and interviews
Upwork Fees and Billing
Understanding Upwork's fee structure
Setting your rates and pricing strategies
Invoicing and getting paid
Communication and Client Management
Effective communication with clients
Setting expectations and deliverables
Managing revisions and feedback
Building long-term client relationships
Upwork's Policies and Guidelines
Upwork's terms of service
Maintaining a good job success score
Handling disputes and resolving issues
Growing Your Upwork Business
Building a strong reputation and profile
Earning and maintaining high ratings
Leveraging Upwork's features and tools
Expanding your service offerings
Bonus Tips and Best Practices
Time management and productivity tips
Networking and collaboration opportunities
Continuing education and skill development
Q&A and open discussion
In our comprehensive Data Analyst course, you'll delve into the fundamentals of data analysis, equipping you with the skills needed to thrive in today's data-driven world. Our expert-led curriculum covers a wide range of topics, including:
○ Data Wrangling: Learn how to clean, transform, and organize raw data for analysis.
○ Statistical Analysis: Master statistical techniques to derive meaningful insights from data.
○ Data Visualization: Harness the power of visualization tools like Tableau and Power BI to communicate findings effectively.
Predictive Modeling: Explore predictive analytics methods to forecast trends and outcomes.
Machine Learning: Gain hands-on experience with machine learning algorithms for data analysis and prediction.
○ SQL: Acquire proficiency in SQL for querying databases and extracting valuable information.
○ Real-World Projects: Apply your skills to real-world scenarios and build a portfolio of projects showcasing your expertise.
With our tailored curriculum and hands-on approach, you'll emerge from our Data Analyst course ready to tackle complex data challenges and drive decision-making processes with confidence.
○ Educational Background: While a specific degree may not always be required, most Data Analyst courses prefer candidates with a background in fields such as mathematics, statistics, computer science, engineering, economics, or related disciplines.
○ Basic Computer Skills: Candidates should possess basic computer skills and familiarity with using software applications. Proficiency in Microsoft Excel is recommended, as it forms the basis for data manipulation and analysis in the course.
○ Technical Requirements: Candidates must have access to a computer with internet connectivity to participate in online lectures, access course materials, and complete assignments.
○ Career Opportunities: Data analysis skills are in high demand across various industries, opening doors to lucrative career opportunities in fields such as finance, healthcare, e-commerce, marketing, and more.
○ Competitive Edge: With the proliferation of data-driven decision-making, possessing data analysis skills gives you a competitive edge in the job market, making you a valuable asset to employers.
○ Versatility: Data analysis skills are transferable across industries and roles, allowing you to explore diverse career paths and adapt to evolving job market trends.
○ Decision-Making Power: By mastering data analysis techniques, you gain the ability to extract actionable insights from complex datasets, enabling informed decision-making and driving business success.
○ Problem-Solving Skills: Data analysis involves critical thinking, problem-solving, and hypothesis testing, fostering the development of valuable analytical skills that are applicable in various contexts.
○ Career Growth: Data analysts often have opportunities for career advancement, with pathways to roles such as Data Scientist, Business Analyst, Data Engineer, and more, as they gain experience and expertise in the field.
○ Contribution to Innovation: Data analysis fuels innovation by uncovering hidden patterns, trends, and correlations that can lead to new product ideas, process improvements, and strategic initiatives within organizations.
○ Job Satisfaction: Working as a Data Analyst can be intellectually stimulating and rewarding, especially when you see the tangible impact of your insights on business outcomes
- Data Analysis Fundamentals: Understanding foundational concepts such as data types, data structures, data cleaning, and data manipulation techniques is essential for effective data analysis.
- Statistical Analysis: Learning statistical methods and techniques for summarizing data, testing hypotheses, and making inferences is crucial for deriving meaningful insights from datasets.
- Data Visualization: Mastering data visualization tools and techniques enables analysts to create visually compelling representations of data that facilitate understanding and decision-making.
- Programming Languages: Proficiency in programming languages such as Python, R, and SQL is essential for data manipulation, analysis, and automation tasks.
- Database Management: Understanding database management systems and SQL querying skills are important for accessing, retrieving, and managing data stored in databases.
- Machine Learning and Predictive Analytics: Exploring machine learning algorithms and techniques empowers analysts to build predictive models and uncover patterns and trends in data.
- Business Intelligence Tools: Familiarity with business intelligence tools like Tableau, Power BI, and QlikView allows analysts to create interactive dashboards and reports for data visualization and analysis.
- Problem-Solving and Critical Thinking: Developing problem-solving skills and critical thinking abilities enables analysts to approach complex data challenges effectively and derive actionable insights.
- Communication and Presentation Skills: Effective communication and presentation skills are essential for conveying analytical findings and insights to stakeholders in a clear and compelling manner.
- Real-World Projects and Case Studies: Engaging in hands-on projects and case studies provides practical experience and helps bridge the gap between theoretical knowledge and real-world applications.
What is Data Analyst training?
Data Analyst training is a structured program designed to equip individuals with the skills and knowledge needed to excel in the field of data analysis. It covers topics such as data manipulation, statistical analysis, data visualization, programming languages, and machine learning techniques.
Who can benefit from Data Analyst training?
Anyone with an interest in data analysis, regardless of their educational background or professional experience, can benefit from Data Analyst training. It is particularly valuable for individuals seeking to pursue careers in data analysis, business intelligence, or related fields.
What are the prerequisites for Data Analyst training?
While specific prerequisites may vary depending on the training program, having a basic understanding of mathematics, statistics, and computer science concepts is beneficial. Proficiency in programming languages like Python or R and familiarity with tools such as Excel and SQL may also be advantageous.
What can I expect to learn in Data Analyst training?
In Data Analyst training, you can expect to learn fundamental concepts of data analysis, including data cleaning, data manipulation, statistical analysis, data visualization techniques, programming languages, and tools used in the field.
What is the duration of the course?
The duration and format of the training is 2 months.
How can I choose the right Data Analyst training program?
When selecting a Data Analyst training program, consider factors such as the curriculum, instructor expertise, mode of delivery, duration, cost, accreditation, job placement assistance, and student reviews and testimonials.
Is certification available upon completion of the training?
Successful participants will get the AIN GenX and Skill Development Council Karachi certificates.
Is there ongoing support or resources available after completing the training?
Yes, participants will get continued support through Email and WhatsApp groups.
Instructor
Irfan Bakaly
Data Analyst
24+ years of experience in Data Analysis
Noor Surani
Data Analyst
Data Scientist
25+ years of experience in Data Analysis
Amynah Reimoo
Data Analyst
2+ years of experience in Data Analysis
Tamkeen Ahmed
Data Analyst
Supply Chain Profession
5+ years of Teaching Experience
Who this course is for:
Data Analyst
Duration
● 2 Months (48 Hours)
● Add-Ons (18 Hours)
Classes
● Online
Schedule
- Days: Mon, Tues, Wed, Thurs
- Timing: 07:30 to 09:30pm (PST)
Starting From
● Monday, 30 December, 2024
Course Fee
● Online: Rs. 12,000/-
One Time Payment
● Excluding the SDC Certificate Fee
Account Details
Bank: Habib Bank Limited
Account Title: AIN GenX
Account No: 5910-70000512-03
IBAN No: PK08 HABB 0059 1070 0005 1203
Secure a Verified Certificate from AIN GenX
Apply for Skill Development Council Certificate
Participants from Various Companies
What do participants say about AIN GenX?
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