The objective of the Data Science Professional program is to equip you with the necessary tools and techniques in understanding the modern business of data and data technologies. These skills will enable you to gather data, process data, and visualize the data at an advanced level. Please scroll down and see more details about this program that also include an outline with sessions break down. Click on "Share More Details" below for us to share more details on monthly class schedules and dates, available discounts, group corporate trainings and much more.


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Program Overview

The ability to extract value from data is one of the most in-demand skills across industries today. The Data Science Professional Program by Docndata Consulting is a hands-on, project-based training designed to build comprehensive capabilities in data analysis, programming, machine learning, visualization, and predictive modeling. Participants will be immersed in real-world use cases and cutting-edge tools across R, Python, SQL, and cloud platforms—preparing them to solve complex problems, generate insights, and make data-driven decisions. Whether you're entering the data space or enhancing your analytical toolkit, this program will help you transition from raw data to actionable intelligence with professional confidence.

Target Audience

This program is ideal for:

  • Aspiring data scientists and analysts building core technical skills

  • Business and operations professionals seeking to use data for decision-making

  • Statisticians and researchers looking to modernize their analytical approaches

  • IT and software engineers expanding into data and AI development

  • University graduates interested in a career in data science or AI

  • Marketing, finance, and logistics professionals aiming to apply predictive analytics

  • NGO, government, and policy officers using data to enhance outcomes and transparency

Expected Targeted Outcomes

By the end of this program, participants will:

  • Gain strong proficiency in R, Python, SQL, and key data science libraries

  • Understand how to clean, manipulate, analyze, and visualize large data sets

  • Build and evaluate machine learning models, including regression, classification, and clustering

  • Use data storytelling and dashboards (Power BI, Tableau, Plotly) to communicate insights

  • Apply predictive analytics to real-world datasets across time, space, and user behavior

  • Design and deploy ETL pipelines and automate workflows for reporting

  • Analyze text data and behavioral patterns using Natural Language Processing (NLP)

  • Work with big data frameworks like Hadoop and Spark for scalable analysis

  • Understand principles of data ethics, privacy, and governance

  • Prepare for real-world data science roles through capstone projects and case studies

Key Core Areas To Be Covered

  1. Programming for Data Science (R, Python & SQL)

    • Syntax, control structures, data structures, and writing functions across key platforms.

  2. Data Wrangling & Manipulation

    • Clean, merge, filter, and transform datasets using dplyr, Pandas, and SQL joins.

  3. Data Visualization & Storytelling

    • Build compelling graphs with ggplot2, Seaborn, Power BI, and Tableau—plus interactive dashboards.

  4. Machine Learning Fundamentals

    • Supervised and unsupervised learning, classifiers, clustering algorithms, and evaluation metrics.

  5. Predictive Modeling & Feature Engineering

    • Regression models, time series forecasting, feature transformation, and validation.

  6. Text Mining & Natural Language Processing (NLP)

    • Sentiment analysis, topic modeling, and recommendation engines using NLP libraries.

  7. Big Data Analytics

    • Introduction to Hadoop, Spark, cloud-based data storage, and distributed computing systems.

  8. ETL & Data Pipeline Automation

    • Build end-to-end pipelines using tools like Apache Airflow for data extraction, transformation, and loading.

  9. Cloud Deployment & Model Scalability

    • Host models on AWS, Azure, or GCP using scalable services and secure architecture.

  10. Ethics, Privacy & Governance in Data Science

    • Navigate GDPR, data anonymization, AI fairness, and responsible data practices in modern analytics.

> This program doesn’t just teach you the tools—it teaches you how to think like a data scientist. Apply real methods to real data to solve real challenges.



Training Program Outline:
https://drive.google.com/file/d/1LMY6ebpTUB04GURp1ktQiMdVqk_SiQN0/view?usp=drive_link


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