Gamma Data Analytics

The next batch of the pay 'ANY AMOUNT' to learn Data Analytics in 10Days opens May 1st.

The announcement is coming early so you can prepare yourself and join this Batch for a 10 days of drilling on relevant data tools that impacts Basic Level proficiency.

See the curriculum......

Day 1 - Introduction to Data Analytics
- Overview of Data Tools
- Data Importation from Web

Day 2 - Data Cleaning, Validation and Formatting
- Clean CSV Data
- Data Parsing in Excel

Day 3 - Data Structures
- Create Data Tables
- Creating Pivot Tables
- Project 1

Day 4 - Data Visualization in Excel
- Understand Data visualization
- Creating Pivot Charts
- Use of remote filters
- Project 2

Day 5 - Data Reports in Excel
- Creating Dashboard ins Excel
- Building KPI charts
- Project 3

Day 6 - Data Visualization with Power BI
- Installing Power BI
- Loading Data from varying sources
Project 4

Day 7 - Dashboading in Power BI
- Drill down functions
- Using map tool in Power BI

Day 8 - SQL for Data Analytics
- Installing MySQL and SSMS
- Creating Tables by Design and commands

Day 9 - Further work with SQL
- Canconating functions
- Importing from Excel to SQL

Day 10 - LinkedIn Optimisation for Data Analytics Jobs
- GitHub creation
- The Data Analytics outlook on SM

This curriculum is carefully designed to get you the get you a Beginner's Foundation in Data Analytics.

Register at https://wa.link/9d2n0s

𝐅𝐈𝐍𝐃 𝐘𝐎𝐔𝐑 𝐍𝐄𝐗𝐓 𝐃𝐀𝐓𝐀 𝐀𝐍𝐀𝐋𝐘𝐒𝐓 𝐑𝐎𝐋𝐄 𝐈𝐍 𝐓𝐇𝐄𝐒𝐄 𝐒𝐈𝐓𝐄𝐒

1. LinkedIn Jobs – https://www.linkedin.com/jobs/
2. Indeed – https://www.indeed.com/
3. Glassdoor – https://www.glassdoor.com/Job/index.htm
4. ZipRecruiter – https://www.ziprecruiter.com/
5. Monster – https://www.monster.com/
6. SimplyHired – https://www.simplyhired.com/
7. CareerBuilder – https://www.careerbuilder.com/
8. Google Jobs – https://www.google.com/ (Search: "Data Analyst jobs near me")

Tech & Data-Focused Job Boards
9. Kaggle Jobs – https://www.kaggle.com/jobs
10. Stack Overflow Jobs – https://stackoverflow.com/jobs
11. HackerRank Careers – https://www.hackerrank.com/work/jobs
12. AngelList Talent (Wellfound) – https://wellfound.com/
13. Otta– https://www.otta.com/
14. Hired – https://hired.com/

Company-Specific Career Pages
15. Amazon Jobs – https://www.amazon.jobs/en/
16. Google Careers – https://careers.google.com/jobs/results/
17. Meta Careers – https://www.metacareers.com/jobs
18. Microsoft Careers – https://careers.microsoft.com/
19. IBM Careers – https://www.ibm.com/employment/
20. Deloitte Careers – https://www2.deloitte.com/global/en/careers/job-search.html

Welcome to Careers at Deloitte Are you ready to apply your knowledge and background to exciting new challenges? From learning to leadership, this is your chance to take your career to the next level. Search and apply for a job today.

💡💡𝐃𝐎 𝐘𝐎𝐔 𝐑𝐄𝐀𝐋𝐋𝐘 𝐊𝐍𝐎𝐖 𝐖𝐇𝐀𝐓 𝐃𝐀𝐓𝐀 𝐀𝐍𝐀𝐋𝐘𝐓𝐈𝐂𝐒 𝐈𝐒 𝐀𝐁𝐎𝐔𝐓?

Let’s clear the air:

🚫 Data Analytics is not just turning numbers into visuals.
🚫 It's not just building dashboards.
🚫 It's not just knowing Excel, Power BI, Tableau, or Google Sheets.
🚫 It's not just writing code in Python or SQL.
🚫 It's not just mastering Power Query, DAX, or TCL.

These are tools. But tools don’t define the craft.

🔍 The true essence of Data Analytics goes far beyond technical skills.

At its core, Data Analytics is about uncovering insights and solving real-world problems. It's about:

✅ Asking the right questions
✅ Framing problems strategically
✅ Connecting dots across complex data
✅ Communicating findings to drive impact

It’s a mindset—rooted in curiosity, context-awareness, and clarity.

A great data analyst doesn't just work with data. They transform raw information into strategic action—across industries like:

🔹 Business → Identify opportunities & predict growth
🔹 Science → Uncover trends for discovery
🔹 Engineering → Optimize systems and processes
🔹 Healthcare → Improve patient outcomes & operations

🧠 Data Analytics is a discipline of discovery. A cycle of exploration, analysis, and iteration.

If you’re stepping into the world of data, don’t just chase tools. Chase insight. Chase impact. Learn with purpose.

💬 What do YOU think is the most misunderstood thing about data analytics?

𝟓 𝐄𝐱𝐜𝐞𝐥 𝐒𝐤𝐢𝐥𝐥𝐬 𝐓𝐡𝐚𝐭 𝐖𝐢𝐥𝐥 𝐈𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲 𝐋𝐞𝐯𝐞𝐥 𝐔𝐩 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐆𝐚𝐦𝐞

📊Whether you're just starting out or already deep in the data world, Excel is still a powerhouse.

Here are 5 essential skills every data analyst should master:

✅ 1. Advanced Functions & Formulas
Think beyond SUM and AVERAGE. Get comfortable with XLOOKUP, INDEX-MATCH, SUMIFS, IFS, and string/date functions. Combine them to create smart, dynamic models.

✅ 2. Pivot Tables & Pivot Charts
Slice, dice, and summarize massive datasets in seconds. Then visualize it all with Pivot Charts for instant insights.

✅ 3. Data Visualization & Conditional Formatting
Make your data speak. Use charts, sparklines, and formatting to highlight patterns, trends, and outliers that decision-makers can act on.

✅ 4. Power Query (Get & Transform)
Automate your data cleaning! Import, filter, merge, and transform data from multiple sources—no manual steps, just refresh and go.

✅ 5. Power Pivot & DAX
Step into advanced analytics with data modeling and DAX formulas. Handle large datasets, build custom KPIs, and analyze like a pro.

🧠 Mastering these will not only boost your productivity—it will set you apart as a high-impact analyst.💬 What’s one Excel skill that transformed the way YOU work with data?

HOW TO TURN YOUR DATA ANALYTICS SKILL INTO A HIGH-EARNING VENTURE

I like to believe you learnt and perfected Data Analytics as Tech Skill for the purpose of earning income. Now, if that is your goal, then you want to be ready to escape the 9-to-5 grind and turn your data expertise into $100K+/year earning advantage per Annum. Here’s the blueprint I have applied in the last few years.

1️⃣Choose Your Money-Making Path
Option A: Freelance Consulting
What to Offer:
- Business Intelligence Dashboards (Power BI/Tableau)
- Excel Automation (Power Query, VBA)
- Custom Data Models (SQL/Python-based)

Where to Find Clients:
- Upwork (Top Rated Plus) → Charge premium rates
- LinkedIn Outreach → Target startups & mid-sized firms
- Cold Email → Create and send mails like “I help [industry], companies reduce costs by 20% using data.”

Option B: Create Digital Products
What to Digital Products are fast Selling:
- Excel/Power BI Templates (e.g., School Results sheet, Financial Models, KPI Trackers)
- Automated Report Systems (Monthly SaaS-style subscriptions)
- Notion Data Trackers (Trending on Gumroad/Etsy)

Best Platforms to find clients:
- Gumroad (Easy setup)
- Etsy (For visually appealing templates)
- Your Own Website (Higher margins)

Option C: Create Online Courses & Coaching
What to Teach that people will buy fast:
- Excel for Business Analysts(Udemy, Teachable)
- SQL for Data-Driven Decisions (Self-paced cohort)
- AI for Data Analysis
- Data Visualization Techniques for Business Leaders
- Excel for Accounting Professionals

Marketing Hack:
Post free tutorials on LinkedIn/YouTube → funnel to paid course.

Option D: Create Agency Model
Services to Offer:
-Data Cleaning & Reporting as a Service (Monthly retainers)
-AI + Analytics Integration (Help businesses use ChatGPT + their data)

How to Scale:
Outsource lower-level tasks (Upwork/Fiverr) → Focus on sales & strategy.

For any of the above money making ventures to work, you need to create a personal brand.

2️⃣ Build Your Personal Brand (The Trust Accelerator)
🚀 Why? People buy from those they know, like, and trust.

To become a go to brand in Data related activities,
- Create a brand page on LinkedIn: Post 2-3x/week (Case studies, data tips, success stories).

- Be on Twitter/X: Share quick insights + engage with data influencers.

- Have a YouTube Channel : 5-minute tutorials (“How to automate X with Python”).

Pro Tip: Repurpose content! A LinkedIn post → Twitter thread → YouTube short.

Now, this is not some tutorials, this is a model many persons on the Tech space have put to test and have made themselves high earners on this street.

SHOULD I USE POWER BI OR TABLEAU FOR DATA VISUALIZATION?

As you begin to practice as a Data Analyst, one of the challenges you will often encounter is the choice of choosing between Power BI and Tableau for data visualization. Both are excellent Data visualization and BI (Business Intelligence) tools but the degree of integration and data handling differs.

Now, to help you choose properly, here are some key considerations to note.

1️⃣Ease of Use and Integration
Power BI is generally easier to use, especially for those already familiar with Microsoft products. It integrates seamlessly with Excel, SharePoint, and other Microsoft tools.

Tableau, on the other hand, has a steeper learning curve but offers more advanced features and customization options.

2️⃣Data Handling and Performance
Tableau is known for its ability to handle large datasets and perform complex calculations quickly. Power BI, while still performant, can slow down with very large datasets.

3️⃣Cost and Pricing
Power BI is generally more affordable, with a free desktop version and a low-cost Pro license ($10/user/month).

Tableau is more expensive, with a Pro version costing around $35/user/month

4️⃣Visualization Capabilities
Tableau is renowned for its advanced data visualization capabilities, offering a wide range of chart types and customization options.

Power BI also offers robust visualization capabilities, but with slightly less flexibility.

5️⃣Collaboration and Mobile Support
Power BI has more built-in collaboration features, including co-authoring and commenting.

Tableau requires third-party tools for similar functionality. Both tools offer mobile apps, but Power BI's app is more robust .

A pro tip is, if you work in an industry that is within the Microsoft ecosystem, power BI is your go tool for Data visualization. If your industry is not Microsoft invested, go for Tableau.

Also, as a Data Analyst it is important you are well acquainted in using both tools.

OUR FIRST RESEARCH COURSE FOR 2025

Most graduate school students spend more than the required duration for their MS and PhD programmes because of the inability to analyze and interpret research data. Some spend hundreds of thousands to find resource persons to work on their data and help to interpret it. Yet, the skill of collecting, analyzing and interpreting Data from research work can just be learnt and mastered in 30days.

Knowing how to Analyze and Interpret Data is an important skill in excelling in Research writing and publication. It is also the one skill that can fetch you alot of money within the academic community.

Our first Research Data Analysis course opens tomorrow and runs for 30days. Participants will learn how to analyze complex data with Ms Excel, Python, R and AI. The addition of AI for research data analysis is new, it releases to you the power to do so much in a few minutes.

To take advantage of this opportunity, register at wa.me/2348035812144

HAPPY INDEPENDENCE ANNIVERSARY TO NIGERIA

At 64th, Nigeria may not have gotten to the peak of what is expected of her, as relates to the stability of the Economy, the quality of life experienced by her citizens and the derivatives of good Governance. But one thing she has achieved is progress.

Thus, as citizens, we are duty bound to continue to wish her success, work for that success and encourage others to join hands in building her.

To this end, a very happy 64th to our Nation Nigeria.

From all of us at Gamma Data Analytics.

DO YOU REALLY KNOW WHAT DATA ANALYTICS IS ABOUT?

Data Analytics is not turning numbers into visuals.

Data Analytics is not designing a Dashboard.

Data analytics is not know how to use Excel, Power BI, Tableau or Google sheets.

Data Analytics is not knowing how to write codes with Python and SQL.

Data Analytics is not knowing how to write Power Queries, DAX or TCL.

We often misunderstand Data analytics is often misunderstood to mean all of the above. This is not correct. The true essence of Analyzing Data lies far beyond these surface-level skills. At its core, data analytics is about extracting valuable insights and identifying trends from data to drive meaningful solutions in various fields, including business, science, engineering, and healthcare.

It's about asking the right questions, framing problems, and leveraging data to inform decisions. A data analyst's role is to delve into complex data sets, identify patterns, and connect the dots to reveal hidden stories. This requires a deep understanding of the context, a curious mindset, and the ability to communicate findings effectively.

Data analytics is not just about technical proficiency; it's a mindset shift. It's about being able to:

- Identify business opportunities and challenges through data-driven insights

- Develop predictive models to forecast outcomes and inform strategic decisions

- Uncover trends and correlations that drive scientific discovery and innovation

- Optimize engineering processes and improve product performance

- Enhance patient outcomes and streamline healthcare operations

In essence, data analytics is a problem-solving discipline that harnesses the power of data to drive impact. It's a continuous cycle of exploration, analysis, and iteration, where data analysts act as storytellers, using data to narrate a compelling story that inspires action.

If you are taking up this journey to learn Data Analytics, kindly be aware of these fasts and learn aright.

HOW TO WRITE PYTHON CODES, PROGRAMS AND SIMULATION ON YOUR MOBILE DEVICE

Google Colab is a cloud-based platform that allows you to write and execute Python code, including complex simulations, using your mobile device. Here's a step-by-step guide to get started:

1. Use a Chrome browser to search colab.research.google(dot)com

2. Sign in with your Google account.

3. Create a new notebook by tapping the "+" icon.

4. Write your Python code in the notebook, using the built-in editor.

5. To run a cell, tap the "Play" button or use the keyboard shortcut "Shift+Enter".

6. For complex simulations, you may need to install additional libraries or dependencies. Use the "!pip install" command to install packages.

7. Use the "%matplotlib inline" command to visualize plots and graphs.

8. To run long-running simulations, you will have to sign up to the "Colab Pro" version, which offers more memory and runtime.

9. Save your notebook regularly by tapping the "File" menu and selecting "Save".

10. To run your notebook on a more powerful machine, tap the "Runtime" menu and select "Change runtime type" to switch to a GPU or TPU accelerator.

Some popular libraries for simulations in Google Colab include:

- NumPy and SciPy for numerical computations
- Pandas for data manipulation
- Matplotlib and Seaborn for visualization
- TensorFlow or PyTorch for machine learning

Example code to get started:
```
import numpy as np
import matplotlib.pyplot as plt

# Simulate a simple harmonic oscillator
t = np.linspace(0, 10, 1000)
x = np.sin(t)

plt.plot(t, x)
plt.show()
```
This code generates a sine wave and plots it using Matplotlib. You can modify and extend this example to suit your simulation needs.

"Are you struggling with data analysis and interpretation in your research?

Do you find it difficult to make sense of your data and draw meaningful conclusions?

You're not alone!

Many researchers face challenges when it comes to data analysis and interpretation. But, with the right skills and knowledge, you can overcome these challenges and take your research to the next level!

Our Research Data Analysis and Interpretation course can help!

In this comprehensive course, you'll learn how to:

- Effectively analyze and interpret your data
- Identify patterns and trends
- Draw meaningful conclusions
- Communicate your findings with confidence

Don't miss out on this opportunity to transform your research!

Sign up now and get instant access to our course materials!

Click the link below to register: [insert link]

"

HOW TO UNDERTAKE A TIME-SERIES RESEARCH WITH STATISTICAL TECHNIQUES

Time series research is a type of research that involves the analysis of data that is collected over a period of time, typically to identify patterns, trends, and relationships.

This type of research is commonly used in various fields, including:

1. Economics: to study business cycles, inflation, unemployment, and GDP.
2. Finance: to analyze stock prices, returns, and trading volumes.
3. Marketing: to examine sales trends, customer behavior, and market responses.
4. Healthcare: to investigate disease incidence, treatment outcomes, and patient survival rates.
5. Environmental Science: to study climate patterns, air quality, and water quality.
6. Social Science: to examine social phenomena, such as crime rates, population growth, and political trends.
7. Engineering: to analyze sensor data, equipment performance, and quality control.
8. Sports: to study team performance, player statistics, and game outcomes.

Time series research can be applied in various ways, including:

1. Forecasting: predicting future values of a time series.
2. Anomaly Detection: identifying unusual or outlier values in a time series.
3. Pattern Recognition: identifying regular patterns or cycles in a time series.
4. Regression Analysis: examining relationships between a time series and external variables.
5. Spectral Analysis: examining the frequency domain of a time series.
6. Machine Learning: using time series data to train machine learning models.
7. Signal Processing: filtering, smoothing, and transforming time series data.
8. Data Mining: discovering hidden patterns and relationships in large time series datasets.

For time series research, some common statistical techniques include:

1. Time series analysis: examining patterns and trends in data over time.
2. Autoregressive Integrated Moving Average (ARIMA) models: predicting future values based on past patterns.
3. Exponential Smoothing (ES): forecasting future values using weighted averages of past observations.
4. Seasonal Decomposition: breaking down time series data into trend, seasonal, and residual components.
5. Frequency Analysis: analyzing the frequency domain of time series data using techniques like Fourier analysis.
6. Vector Autoregression (VAR) models: examining relationships between multiple time series variables.
7. Impulse Response Analysis: analyzing the response of a time series to external shocks or interventions.
8. Forecasting: predicting future values of a time series using various techniques like ARIMA, ES, and neural networks.
9. Anomaly Detection: identifying unusual or outlier values in a time series.
10. Change Point Detection: identifying significant changes or shifts in a time series.

Some common software used for time series analysis includes:

1. Python libraries: pandas, statsmodels, scikit-learn, and PyAlgoTrade.
2. R libraries: zoo, forecast, and ts.
3. Excel: built-in time series analysis tools and add-ins like Solver and Analysis ToolPak.
4. Specialized software: EViews, Stata, and SAS.

Remember to choose the appropriate techniques and software based on your research question, data characteristics, and level of expertise.