Data Science & Advanced Analytics

With the emergence of new technologies and advancements in AI, businesses can now make faster and proactive decisions using their actual data and on their top priority opportunities.

Discover how Silk Data provides Data Science & Advanced Analytics and services with cutting-edge algorithms and tools to help businesses improve their decision-making processes and gain insights from their data.

Discover Silk Data provides Data Science & Advanced Analytics
A mix of Data Science and Analytics leads to increased efficiency

Data Science is the field that involves using advanced statistical and computational methods to extract insightful information from data. Advanced Analytics refers to the application of various analytical techniques and technologies to improve business operations. Using a mix of Data Science and Analytics leads to increased efficiency and the automation of processes, resulting in improved productivity.

Text Summarization
Communal service
Contract Analysis
Supermarket chain
AI-assisted Search
Large-scale image search system
Predictive Analytics
Web Crawling
Semantic Map

Why do you need Data Science and Advanced Analytics?

  • To gain insights from large and complex data sets:
    With the increasing amount of data generated by businesses, it can be challenging to extract meaningful insights from it. Data Science helps businesses to analyze and understand this data, uncover patterns and trends, and make informed decisions based on the insights gained.
  • To improve decision-making processes:
    By using Data Science, businesses can make more informed decisions based on data-driven insights rather than relying on intuition or guesswork. This can lead to better outcomes and improved performance. 
  • To enhance customer experiences:
    Data Science can help businesses to understand their customers better by analyzing their behavior, preferences, and needs. This can enable businesses to personalize their offerings and improve the overall customer experience. 
  • To identify new opportunities:
    Data Science can help businesses to identify new opportunities for growth and innovation by analyzing market trends, customer behavior, and other relevant data. 

What the process involves  

Data Science and Analytics is an iterative process that can be broken down into several stages. These include defining the problem, collecting, and cleaning data, exploring and visualizing data, creating predictive models, and communicating results.

  • Step 1: Define the Problem
    Our team will help determine what you want to achieve with your data and identify the questions necessary to clarify the situation.
  • Step 2: Collect and Clean Data
    Data preprocessing is a crucial step in data science as it is the foundation for generating correct outcomes. Raw data is collected from many data sources, for instance corporate databases, web pages, or other public sources and in various special formats like MS Word, or PDF.

    Next step is cleaning raw data from abnormal values, missing or incomplete parts, removing duplicate records and misprints, checking for errors in the data, standardizing its format, and transforming the data into a usable configuration for analysis. Clean data is essential to ensure the models are reliable, have higher accuracy, and can be trained effectively. 
  • Step 3: Explore and Visualize Data
    Our next step is investigating the data to identify patterns, trends, and anomalies, and create visualizations to help others understand your findings.
  • Step 4: Create Predictive Models
    Then using a variety of modeling techniques, we build accurate models that make predictions based on your data.
  • Step 5: Communicate Results
    The final step in Data Science is presenting your results in a clear, concise, and compelling way so you can make informed decisions based on the insights.
Data Science and Advanced Analytics process

Our Data Science & Analytics industries:

Analyzing customer data to develop targeted marketing campaigns and identify new markets and potential buyers.
Analysis of trends and interests.
By analyzing data on customer behavior and preferences, marketers can offer personalized ads recommendations. Moreover, marketers can get information on their target audience to make more relevant content.

Recommendation of titles to readers.
Recommendation systems work by analyzing large quantities of data related to the books that readers have read and enjoyed, as well as their demographic and behavioral data.
Advanced search and navigation in online texts.
Advanced search and navigation in online texts refer to the features that allow users to search, filter, and navigate digital texts in a more efficient and effective way.

Testing transaction data to identify patterns and trends in purchasing behavior to improve inventory management and sales forecasting.
Detect patterns in customers’ behavior to predict possible churn and improve their loyalty.
Prepare personalized recommendations for better service to customers.

Implementing predictive models to identify patients that are likely to develop certain diseases, and identifying treatments that are most effective for these patients.

Why Silk Data for Data Science & Analytics services?

Why Silk Data for Data Science & Analytics services?

Our team of  Data Science professionals are ready to take on the most challenging data tasks of any type and size. We’re experienced in delivering data mining and explanatory analysis projects throughout their entire pipeline, including collection, extraction, normalization, analysis, modeling, evaluation, and visualization of results. 

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