Data Analyst , Data Scientist , ML Models

Driving Client Success by leveraging Digital Marketing Domain Expertise and Data Science Proficiency for Optimal Impact.

Contact Me
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Python Numpy   Pandas   Matplotlib Jupyter Scikit-learn Keras Tableau Prophet PyMC mySQL

Data Preprocessing - Exploratory Data Analysis (EDA) - Probability Theory and Statistics - Linear Algebra - Algorithms - Preprocessing Data - Time Series Analysis - Machine Learning Algorithms - Supervised & Unsupervised Learning - A/B Testing - MMM - Business Intelligence & Analytics

As a budding data scientist eager for exciting projects, I am eager to apply my skills and expertise to make a meaningful impact. With a passion for data analysis and machine learning, I am committed to leveraging data-driven insights to solve real-world challenges. By collaborating with teams,  organizations, and initiatives that align with my interests, I am driven to contribute to positive change and innovation. Through ‘project’ volunteering, I have the opportunity to expand my knowledge, grow my network, and gain valuable experience in diverse domains. I am excited to embark on this journey of exploration, learning, and making a difference through data science. Join me as we harness the power of data to drive positive change together! 

🚀 I'm eager to tackle a data science project for free!
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  • num = 7 if num % 2 == 0: print(num, "is even")
  • else: return fibonacci(n-1) + fibonacci(n-2)
  • def factorial(n):
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  • return n * factorial(n-1)
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  • print("The factorial of", num, "is:", factorial(num))
  • for i in range(10, 0, -1):
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  • for i in range(1, 11):
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Marketing data scientists leverage machine learning techniques to analyze vast amounts of data collected from various sources such as customer interactions, website visits, and social media engagement

Why Marketing Data Scientist?

  1. Data-Driven Decision Making: Marketing data scientists leverage machine learning techniques to analyze vast amounts of data collected from various sources such as customer interactions, website visits, and social media engagement. By extracting valuable insights from this data, businesses can make informed decisions to optimize their marketing strategies & implement Business Intelligence & Analytics.

  2. Personalized Marketing Campaigns: With the help of data science, marketers can segment their target audience more effectively based on demographics, behavior, and preferences. This allows them to tailor marketing campaigns to specific customer segments, leading to higher engagement and conversion rates. For example, an e-commerce company can use machine learning algorithms to recommend personalized product suggestions to individual customers based on their past purchase history.

  3. Predictive Analytics: Marketing data scientists can build predictive models to forecast customer behavior and anticipate market trends. Byidentifying patterns and trends in historical data, businesses can predict future outcomes, such as customer churn or sales trends, and take proactive measures to address them. For instance, a subscription-based service can use predictive analytics to identify customers who are likely to cancel their subscriptions and implement retention strategies to prevent churn.

  1. Optimizing Marketing Budget Allocation: Data science techniques allow marketers to optimize their budget allocation by identifying the most effective marketing channels and campaigns. Through A/B testing and attribution modeling, marketing data scientists can determine which marketing channels drive the highest return on investment (ROI) and allocate resources accordingly. For example, a digital advertising campaign can use machine learning algorithms to allocate ad spend across different platforms based on their performance metrics, such as click-through rates and conversion rates.

  2. Enhancing Customer Experience: By analyzing customer feedback and interaction data, marketing data scientists can gain insights into customer preferences and pain points. This information can be used to improve product offerings, streamline customer service processes, and enhance overall customer experience. For instance, a retail company can use sentiment analysis techniques to analyze customer reviews and identify areas for product improvement or service enhancement.

🙌🏼 How can I help you ?

Comprehensive Data Analysis:

Utilizing exploratory data analysis and advanced data visualization techniques to uncover valuable insights hidden within complex datasets. By diving deep into the data, I can uncover trends, patterns, and correlations that drive informed decision-making.

Advanced Python and Machine Learning Techniques:

Leveraging Python programming alongside Machine Learning Algorithms to extract meaningful information and predict future outcomes. From regression and classification to clustering and neural networks, we apply cutting-edge techniques to extract maximum value from your data.

Actionable Insights Delivery:

Translating data findings into actionable insights that directly impact business strategy. By combining analytical rigor with industry expertise, we provide clear recommendations and strategies to optimize marketing efforts, enhance customer experiences, and drive business growth.

Advanced Attribution Modeling:

Integrating digital marketing expertise with data science methodologies to develop sophisticated attribution models. By accurately attributing conversions and understanding the impact of each marketing touchpoint, we optimize budget allocation, enhance campaign performance, and drive higher ROI. Whether it’s multi-touch attribution models or marketing mix modeling (MMM), we ensure data-driven decision-making to maximize marketing effectiveness.
My projects 👩🏻‍💻

Data science projects that leverage data-driven insights to optimize their marketing strategies, improve customer engagement, and drive business growth. Projects where I appled machine learning and data science techniques to marketing and advertising data, businesses can gain a competitive advantage in today’s digital landscape!

Work with Me!

FoodHub Order Analysis

Exploratory Data Analysis & Visulaization for Buisness Insights

E-Portfolio

Bank Customer Segmentation

Data Visualisation, EDA, PCA & t-SNE for bank customer delivery model

E-Portfolio

Marketing Campaign Analysis

Data Analysis & Visualization/Unsupervised Learning, Clustering Techniques

E-Portfolio

Let's Work Together

CONTACT ME ⟶

© Data Scientist Portfolio & Marketing Mix Modelling 2025