Introduction to Data Science

This DIXONTECH course offers a comprehensive introduction to Data Science, providing participants with the fundamental concepts, methods, and tools used in data-driven analysis and decision-making. The program covers essential topics including data collection, data cleaning, statistics, programming basics, and visualization. By the end of the course, learners will understand how to use data to generate insights and support strategic objectives effectively.

TRAINING TOPICS:

• Fundamentals of Data Science and Analytics

• Data Collection and Preparation

• Exploratory Data Analysis and Statistics

• Data Visualization and Communication

• Applying Data Science in Real-World Contexts

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO:

By the end of this DIXONTECH training, participants will:

• Understand the data science workflow and process

• Clean, prepare, and analyze structured datasets

• Apply descriptive and inferential statistics

• Visualize data using modern tools and techniques

• Build basic predictive and analytical models

• Communicate data insights effectively

• Apply data science to business and research

TRAINING IS TAILORED TO:

This course is designed for:

• Entry-level data professionals and analysts

• Business and operations managers

• Students and researchers using data

• Financial and marketing professionals

• IT and software developers

• Project and strategy coordinators

• Anyone interested in beginning a data science career

TRAINING METHODOLOGY:

DIXONTECH employs a balanced approach combining theory, practical exercises, and data projects. Participants gain hands-on experience with analytical tools like Python, Excel, and Power BI while exploring real datasets. Group discussions, guided labs, and short case studies reinforce understanding and practical application of core data science principles.

DAY 1: Fundamentals of Data Science and Analytics

• Defining data science and its business value

• Understanding the data science lifecycle

• Key components: data, models, and insights

• Types of data and analytical methods

• Overview of tools and technologies in data science

• Data-driven decision-making principles

• Roles and responsibilities in data teams

DAY 2: Data Collection and Preparation

• Identifying and gathering relevant datasets

• Data formats: CSV, JSON, and SQL databases

• Data cleaning, validation, and transformation

• Handling missing and inconsistent data

• Feature selection and data enrichment techniques

• Introduction to Python libraries for data manipulation

• Case study: preparing data for analysis

DAY 3: Exploratory Data Analysis and Statistics

• Understanding descriptive statistics and measures

• Data distribution, variance, and correlation

• Exploratory analysis using graphs and summaries

• Detecting trends, outliers, and anomalies

• Hypothesis testing and statistical inference

• Introduction to probability concepts

• Practical lab: analyzing and summarizing datasets

DAY 4: Data Visualization and Communication

• Best practices in data storytelling and presentation

• Choosing suitable visualization types

• Using Excel, Power BI, and Python visualization tools

• Creating dashboards and interactive visuals

• Communicating findings to technical and non-technical audiences

• Avoiding visual misrepresentation and bias

• Project: design a business insight dashboard

DAY 5: Applying Data Science in Real-World Contexts

• Linking data science to business problems

• Introduction to machine learning concepts

• Predictive analytics and simple model building

• Evaluating model performance and accuracy

• Data ethics and governance considerations

• Future trends in data science and AI

• Final project: data insight presentation and feedback