Data science services
Data Science Consulting
Data Explosion: Hitting Close To 180 Zettabytes By 2025
By 2025, the world is projected to generate around 180 zettabytes of data—equivalent to 200 trillion DVDs—according to recent studies. 90% of this data has been created in just the past 5 years. While many businesses collect vast amounts of data, only a few can tap its full potential for valuable insights. Without data science, you’re missing out on key trends in customer behavior, market changes, and business performance. Data science uncovers complex patterns, reveals hidden insights, and predicts future trends. From forecasting sales to understanding consumer behavior, it turns raw data into actionable business intelligence.
We begin with a design thinking workshop to understand your business needs, Data strategy and what you’re missing out today without actionable business intelligence.
First, we prepare and prime your data using Python, R and set up Data analysis using Databricks and AWS. We use Apache Spark and Kafka in our data pipeline to ensure a steady, reliable flow of data. Once your data is primed, we apply machine learning and advanced analytics to build predictive models that help you forecast trends, optimize processes, and drive smarter business decisions. We create custom solutions for your specific business needs using TensorFlow, scikit–learn, and other advanced ML libraries.
What You Can Expect:
98%
Accurate Predictions
5x
Faster Analysis
3×
Faster Data Processing
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Data Science Consulting Services
Data can provide immense value, but only if it’s properly analyzed and used in moments that matter. Without a Data strategy, businesses may struggle to make sense of complex datasets and miss valuable opportunities. At Codewave, we provide Data Science Consulting Services that ensure your data works for you.
Many businesses collect massive amounts of data but still rely on gut feelings to make decisions. At Codewave, we start by listening closely to your needs and apply data science where it can have the greatest impact. Whether it’s identifying customers at risk of churn, optimizing processes that affect customer experience, or predicting machine failures that could disrupt revenue, we focus on what matters most.
We assess your data sources and recommend the right tools and technologies to transform that data into actionable insights. Our consulting team designs roadmaps for your Data-science success, outlining the best approaches for data preparation, model development, deployment and continuous evolution. We provide recommendations on technologies like Python, Scikit-learn, and PyTorch based on your unique use case, and suggest infrastructure solutions such as AWS, Azure, or on-premise options to support your data-driven goals.
Collecting the right Data and high-quality data is the heart of any successful Data strategy. Without valuable, reliable data, even the most sophisticated analyses can lead to misguided decisions or low confidence in conclusions.
At Codewave, we start by identifying the most relevant data sources for your business needs. We work closely with your team to set up efficient data collection systems, whether it’s integrating customer data from your CRM, pulling production metrics from factory systems, or aggregating sales data from platforms like Salesforce, SAP, Oracle.
We use Apache Kafka for real-time data streaming and integration, allowing you to gather data continuously, in real-time from various sources. We use web crawling techniques with BeautifulSoup and Scrapy to extract the most current data from websites.
Imagine a retail store that wants to keep its shelves stocked efficiently. By collecting real-time sales data from each store, the company can quickly see which products are selling fast and need restocking. This timely information helps the store avoid running out of popular items and reduces excess inventory of slow-moving products.
Raw data is unprocessed and often unsuitable for sophisticated analysis and interprettations, as it is likely filled with duplicates, missing values and errors making it difficult to extract meaningful insights.
At Codewave, we transform your raw data into a clean, reliable asset. We start by using Python scripts to identify and correct common data entry errors, such as typos and incorrect formats, ensuring consistency and accuracy. We then use SQL queries for duplicate removal to efficiently identify and eliminate redundant records, guaranteeing that each entry in your dataset is unique and reliable.
To handle missing values, we employ statistical methods and Machine Learning techniques to intelligently fill in gaps, ensuring your dataset is complete and ready for analysis.
For example: Say, an eCommerce company with customer data has many duplicates and missing pincodes. Cleaning this data, populating accurate pincodes ensures each customer has a detailed profile, helping you improve customer service.
Raw data needs to be cleaned, prepared and analyzed for generating actionable business intelligence. Without proper analysis, data is just numbers on a screen, failing to provide the direction needed for timely, strategic decision-making.
We start by identifying key business questions and the data that will help answer them. Using Python and R, we perform exploratory data analysis (EDA) to uncover trends (e.g., shifts in sales or customer behavior over time), patterns (such as recurring buying habits or seasonal variations), and correlations hidden within the data (like relationships between socio/economic/political/cultural changes and customer footfall / sales).
We use Tableau and Power BI to visualize complex data, creating clear, actionable insights that help your team quickly understand key metrics and make informed, data-driven decisions.
For instance, analysis of past sales data shows that certain products perform better during specific months, like increased demand for outdoor gear in the summer. This insight helps businesses plan seasonal inventory and adjust marketing campaigns to target peak sales periods.
When your marketing team is guessing which products will sell more this season, and your sales forecasts are based on gut feeling rather than data, you’re missing out on valuable opportunities. Predictive analytics helps you understand what’s coming next so you can take informed decisions eliminating the risks of guesswork.
We solve this with Predictive Analytics, which allows you to forecast future trends based on your historical data. By using machine learning models and statistical techniques, we help you predict trends, customer behaviors, and market shifts with high precision. Python, R, and Scikit-learn allow us to build and fine-tune predictive models that provide insights into what’s coming next, with more than 90% accuracy.
For example, a retail company wants to optimize its stock levels during the holiday season. By analyzing past sales data, customer buying patterns and other influential factors like economy / inflation, predictive models forecast which products is likely to see a surge in demand. This allows the retailer to adjust inventory levels, pricing, deals, ensuring that popular products are stocked without overstocking items that won’t sell.
As your business grows in size and complexity, and your strategic goals upgrade, you will need newer technology capabilities and your Data stack needs to adapt as well. What worked yesterday may no longer meet demands of today, and you need to constantly improve your models for high precision business management and remaining ahead of the game.
We use TensorFlow to improve deep learning models and XGBoost for better predictive performance. For model retraining and versioning, we rely on Docker for containerization and Kubernetes for scaling across environments. Our approach ensures your models remain agile, accurate, and effective, no matter how your business grows.
Example: A financial institution uses a fraud detection model that starts losing accuracy due to newer threats and evolving fraud patterns. By enhancing the model with new data features and retraining it, the solution stays effective and continues providing timely, accurate fraud detection in real time.
Once your data science solutions are live, continuous support is essential to ensure it works as expected and continues to self improve. Over time, issues can arise—models may drift, predictions may become less accurate, or the system might fail to learn from new data. Without regular monitoring and updates, these issues can lead to unreliable insights, delayed decision-making and ultimately, missed opportunities.
We provide Data Science Solution Support by continuously monitoring model performance to ensure they stay accurate and reliable. Datadog helps track performance metrics in real-time, allowing for quick identification of any issues. For model updates, we use MLflow to manage versions and facilitate seamless retraining. By integrating fresh data into your models, we ensure they adapt to new trends and continue delivering insights that align with your business objectives.
Example: An e-commerce company sees lower conversion rates as customer preferences change. The model is retrained with evolving customer behavior and tested, ensuring accurate recommendations and boosting conversions.
We help businesses implement data science solutions that deliver tangible outcomes. Our team takes care of integrating models directly into your business processes, and measure impact / ROI. We ensure the solutions implemented bring visible impact like reduced delays in decision-making or saving time/costs, by deploying Agentic AI for ongoing analysis and automation. For model development, we use TensorFlow for deep learning and complex algorithms, and Scikit-learn for building and refining predictive models. For smooth deployment and scalability, we rely on cloud platforms like AWS and Azure to ensure the solution fits seamlessly into your infrastructure.
Example: A retail company needs to improve inventory management. By implementing a predictive demand forecasting model built with Scikit-learn, the system automatically adjusts stock levels based on real-time data, optimizing inventory and reducing overstocking. The model is integrated into the company’s existing supply chain system, providing instant, actionable insights.
Creating a machine learning model can take time and resources, but sometimes businesses need to see quick results. The Minimum Viable Model (MVM) approach helps solve this by building a simple version of the model that can still deliver useful insights right away, without waiting for a more complex solution.
We begin by identifying the key outcomes that matter most to the business. We use Python for data cleaning, then Pandas, and SQL to structure and organize the data. Next, we use Scikit-learn to build a simple model that generates predictions to meet the desired outcomes.
After testing the model on a small dataset, we use Python for evaluation and Jupyter Notebooks to fine-tune the model, enhancing accuracy and preparing it for scaling.
Say a company wants to predict customer churn. By segmenting customers based on their urgency / past activity, analyzing other data like account age, brand affinity / sentiment, a model identifies high-risk customers. The results help sharpen your customer retention efforts, and the more you use the model and feed data & feedback, the better it gets over time.
The Tech Behind Your Data Transformation
| Process | Technology Used | Purpose |
| Data Cleaning | Python, Pandas, SQL | Clean and structure raw data |
| Data Organization | Pandas, SQL | Organize and structure data for analysis |
| Model Building | Scikit-learn, Python | Build predictive models based on key features |
| Model Evaluation | Python, Jupyter Notebooks | Evaluate model performance and fine-tune it |
| Model Refinement | Python, Scikit-learn, Jupyter | Refine and optimize model accuracy |
| Scalability & Deployment | AWS, Azure, Docker | Deploy and scale models for real-world applications |
| Visualization | Tableau, Power BI | Visualize model results and key metrics for decision-makin |
Ready to Make Better Decisions with Your Data?
Why Codewave? Because We Get The Worry
We understand your anxiety to grow and the fear of becoming irrelevant and stagnant. You could go anywhere for data science consulting, but here’s why Codewave is the partner you actually need:
Data Science: Shaping the Future of Every Industry
| Industry | How Data Science Consulting Benefits |
| Healthcare | Data science helps in improving diagnostic accuracy, patient care, predicting patient recovery and optimizing resource allocation. Machine learning models enable early detection of diseases, personalized treatment plans, and efficient patient management. |
| Energy | By analyzing energy consumption patterns and operational data, data science helps predict energy demand, optimize storage / distribution, plan energy generation to meet changing demand and encourage optimal energy usage. |
| Education | Data science can predict learner challenges, adapt the pace/style of content to improve learning outcomes, and personalize learning experiences. It also helps in analyzing needs of learners and allocating educators & resources accordingly. |
| Fintech | In fintech, data science enables threat / fraud detection, risk management, and accurate credit scoring. Predictive models can also help in sensing market trends and recommending better investment strategies. |
| Insurance | Data science optimizes underwriting, risk assessment, and claims processing by analyzing vast amounts of data to predict future risks and simplify operations. Machine learning models can predict volume of claims, potential frauds and improve profitability. |
See Our Data Science Success Stories
Every successful data science solution begins with solving real business challenges. Our case studies highlight how we:
- Transformed raw data into actionable insights that drove growth
- Enabled proactive decision-making through predictive models
- Optimized business operations by automating complex tasks
- Enhanced customer experiences with personalized recommendations
- Improved accuracy and efficiency with machine learning-driven solutions
We transform companies!
Codewave is an award-winning company that transforms businesses by generating ideas, building products, and accelerating growth.
What to expect
What to expect working with us.
Frequently asked questions
We follow a design-thinking led approach that combines data expertise with user experience. Our process involves understanding your business challenges, ideating solutions through collaborative workshops, prototyping, and implementing scalable solutions while ensuring continuous feedback and improvement.
Implementation timelines vary based on project complexity, ranging from 2-3 months for basic analytics solutions to 6-12 months for comprehensive enterprise implementations. We provide detailed timeline estimates during initial consultations based on your specific requirements.
We implement industry-standard security protocols and comply with global regulations including GDPR, HIPAA, and CCPA. Our solutions include encryption, secure access controls, and regular security audits. We also provide documentation for compliance requirements.
Yes, our solutions are designed to integrate seamlessly with your existing infrastructure. We support integration with various databases, cloud platforms, and business applications through APIs and custom connectors while maintaining data integrity and security.
ROI varies by implementation but typically includes improved efficiency (20-30%), reduced operational costs (15-25%), and enhanced decision-making capabilities. We work with you to define specific KPIs and track progress throughout the implementation.
We employ robust data cleaning and preparation processes, including automated quality checks, data validation, and standardization procedures. Our team works with you to establish data governance practices and maintain high-quality data standards.
We offer comprehensive post-implementation support including system monitoring, performance optimization, regular maintenance, and training for your team. Our support packages can be customized based on your needs.
Our solutions are built using cloud-native technologies and modular architecture that allows for easy scaling. We consider future growth requirements during the design phase and implement solutions that can adapt to increasing data volumes and user demands.
