I come from the world of finance, a dynamic sector where every decision is crucial. With a Master's degree in Finance specializing in econometrics and having obtained the first two levels of the CFA, I have developed in-depth expertise and an insatiable passion for data analysis.

### What is data analysis ?
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to extract useful insights, suggest conclusions, and support decision-making.

### What is a market study ?

A market study is a systematic research process that involves collecting, analyzing, and interpreting information about a specific market. Its goal is to understand consumer needs, preferences, and behaviors, identify market trends, assess competition, and determine opportunities and risks associated with a product or service.

### Why is a data-driven market study relevant ?

~A data-driven market study is relevant because :~

- Data provides concrete evidence, enabling businesses to make fact-based decisions rather than relying on assumptions or intuition.
- Data helps gain a deep understanding of consumer behavior, preferences, and needs.
- With data, different market segments can be identified, allowing for product or service personalization and more targeted, effective marketing strategies.

### How were market studies conducted before data analysis (economics, IT, statistics) ?

Before the era of big data and advanced analytical tools, market studies were conducted using methods based on manual data collection, direct observation, and less quantitative analysis.

- **Surveys and polls**
- **Direct observation**
- **Focus groups** (Focus groups provided qualitative feedback)

The shift to modern data analysis has significantly enhanced the depth, speed, and accuracy of market studies, enabling a much more nuanced and dynamic understanding of markets.

### How is a market study conducted using data analysis ?

1. **Defining Objectives**
2. **Data Collection**
3. **Data Cleaning and Preparation**

Ensure data quality by removing or correcting missing values, duplicates, errors, or inconsistencies. This process is often done using tools like **Python (Pandas)** or **R**.

4. **Exploratory Data Analysis (EDA)**

Use descriptive statistical techniques to understand the structure, trends, anomalies, and relationships within the data. Data visualization tools like **Matplotlib** and **Seaborn** in Python or **ggplot2** in R help illustrate insights.

5. **In-depth Analysis Based on the Objective**

6. **Visualization and Reporting**
7. **Interpretation and Decision-Making**

8. **Validation and Iteration**
Validate conclusions with field tests or additional studies, refining the analysis if necessary to ensure accuracy and relevance.

### Commonly Used Tools

- **Python & R** → For data analysis
- **SQL** → For data management
- **Tableau, Power BI** → For data visualization
- **Scikit-learn, TensorFlow** → For predictive modeling

Data analysis in market research not only enables a deeper and more dynamic understanding of markets but also enhances the ability to anticipate trends and respond quickly to changes.

With this service, I offer to carry out a data analysis with the aim of carrying out a market study.
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### My Work Process :

**Define the Market Study Objective**
You will clearly define the study's goal (we can also do this together—just send me a private message).

**Provide the Data for Analysis**
You will supply the dataset to be analyzed.

**Validation of Methods & Tools**
Before confirming the order, I will share my analysis methods and the tools/software to be used.

**Agreement on Report Format & Visualizations**
We will finalize the format, length, and type of visualizations to be included in the final report.

Your data will remain on my device and will not be uploaded to any cloud service.
The amount of data I can process depends on my system's capabilities.
The dataset should not exceed **100,000 rows** and **100 mo** in size.

**Questions ?** Feel free to send me a message!
For a personalized solution tailored to your needs, don't hesitate to reach out.

### Why Choose My Services ?

**Diverse Experience** – A strong background in data analysis, research, and report writing, following a structured and methodical approach thanks to my data analyst training.

**Turning Data into Insights** – My role is to transform your raw data into valuable insights, helping you achieve your goals with precision.

On **ComeUp.com**, you can find service providers specializing in Google Ads, Kozikaza (a 3D illustration tool), OnlyFans, MYM, OFM, and even Amazon KDP professionals!

However, **as a freelance data analyst, my expertise is solely focused on marketing and big data.**

### Data Analyst vs. Data Scientist

Data Analyst Data Scientist
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Analyze and interpret data Build predictive models
SQL, Excel, Tableau, Power BI, basic statistics. Python, R, machine learning, deep learning, advanced statistics.
Excel, SQL, Power BI, Tableau, Google Analytics. Python, R, TensorFlow, PyTorch, Hadoop, Spark.
Bachelor's/Master's degree in statistics PhD/Master's degree in data science

~**Know that as a data scientist, I master both Data Analyst and Data Scientist tools !**~

As for my data analysis experience, I have completed various tasks for my clients, in addition to those carried out during my academic and professional journey.

Among others

**Analysis of flight delays and cancellations**
This project involved creating a report on delays, cancelled flights, no-show passengers, and other variables, and correlating them with ticket pricing.

**Financial data analysis**
As part of my finance training, I was tasked with manipulating, analyzing, and making projections of financial data using primarily Excel, VBA, Python, and R.

**Operating systems and code**
This project involved studying the correlation between the use of different operating systems and the efficiency of code created by developers in the financial markets.

**Qualitative and quantitative analysis of patient experience during a hospital stay**
Using a database created by surveying hospital patients, the task was to analyze and draw conclusions about patients’ hospital experiences. The analysis was focused on R and Excel VBA.

**Real estate econometrics**
Using SQL and Python, the project involved creating a prediction model for real estate prices in a specific geographic area.

**Reproduction of technical analysis from a scientific article**
As part of tutoring, I helped psychometrics students reproduce the quantitative analyses from a scientific article chosen in advance.

Some testimonials from my clients on ComeUp
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WASATE “High quality service”
pierre_de_Cayron “Work done very well, on time, easy communication and without any problems”
ethaanzy “Excellent work, thank you very much!”
Nathan_Begkoyian “Thank you, the work perfectly meets my expectations”
Morpheis1 “Great! Professional and efficient service, I highly recommend!”

### Why order from me and via the comeup.com platform ?

By choosing to order via comeup.com, you are opting for the excellence and transparency of an ecosystem dedicated to professionals.

- Quality and expertise: The platform brings together hand-picked freelancers, recognized for their expertise and know-how in various fields. Each service is evaluated by a community of satisfied customers, thus ensuring a high and reliable level of quality.

- Security and trust: Comeup.com guarantees a secure and transparent ordering process, from the initial contact to the final payment. Reviews and ratings from previous clients give you an overview of the quality of service, reinforcing confidence in your decision.

- Support and guidance: Thanks to responsive customer service and personalized follow-up, the platform ensures that each project runs smoothly. You benefit from tailor-made support to transform your needs into concrete results.

Visibility and simplicity: Order in a few clicks without having to manage quotes or invoices. The process is transparent and easy to use.
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Opting for my services via comeup.com is therefore choosing efficiency, reliability, and a win-win partnership, where every detail counts to make your project a success.

### FAQ (Frequently Asked Questions)

**How do you guarantee the confidentiality of my data ?**
Your data remains exclusively on my device and is never uploaded to a cloud. Confidentiality and security are my top priorities.

**What tools do you use for data analysis ?**
I primarily use **Python and R** for analysis, **SQL** for data management, **Tableau and Power BI** for visualization, and **Scikit-learn and TensorFlow** for predictive modeling.

**How can I contact you to discuss my specific needs ?**
You can reach out to me via private message to discuss your specific needs and get a tailored solution.

**Can we define the market study objectives together ?**
Absolutely ! If you're unsure about the precise objectives of your study, we can define them together. Just send me a private message to start the collaboration.

**Why should I choose a data-driven market study over traditional methods ?**

Data-driven market studies offer several key advantages:

**Evidence-based decisions** – Data provides solid proof, replacing intuition with facts for more reliable decision-making.
**In-depth consumer understanding** – Data analysis helps uncover consumer behaviors, preferences, and needs in detail.
**Precise market segmentation** – Data enables the identification of specific market segments, allowing for customized products, services, and marketing strategies for maximum effectiveness.
**Trend anticipation** – Advanced analysis helps detect emerging trends and predict market shifts, giving you a competitive edge.
**Efficiency and speed** – Modern tools allow for faster and more precise market studies compared to traditional methods.

My service ensures you benefit from all these advantages !

### If you've ever wondered :

**Is it possible to anticipate the evolution of your market through predictive analysis ?**

~How can you optimize the quality of your market study dataset by efficiently managing missing values and outliers ?~

Which EDA (Exploratory Data Analysis) techniques should you apply to extract relevant insights from your raw data ?

**How can you use Python or R to automate and deepen the statistical analysis of your market ?**

~Can predictive modeling methods truly anticipate future trends in your industry ?~

How do you structure a robust data analysis workflow for a data-driven market study ?

**How can you integrate data from heterogeneous sources (CRM, web analytics, open data) for a 360° market analysis ?**

Then, you're in the right place ! As a data analysis specialist, I offer to transform your raw data into clear and actionable insights to guide your strategic decisions.

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