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Introduction to Data Science Training
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Drive Team Excellence with Introduction to Data Science Corporate Training

Data Science combines statistical analysis, machine learning, and visualization to extract meaningful insights from complex data sets. Data science is essential for interpreting complex data, revealing actionable insights, solving critical challenges, and equipping teams with the skills needed for strategic advantage in a rapidly evolving digital terrain.

Introduction to Data Science training course is vital for developing foundational analytics and machine learning skills, enabling teams to analyze data effectively, and apply insights for organizational growth and efficiency.

Edstellar's instructor-led Introduction to Data Science training course offers virtual/onsite training modes conducted by industry experts with extensive experience in data science. The course includes a customizable curriculum that addresses your organization's needs and hands-on exercises that simulate real-world problems. The course delivers theoretical knowledge and practical application, ensuring teams can apply data science tools and functionalities in real-world scenarios.

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Skills Your Employees Will Gain

These are the core, hands-on capabilities your team builds during the program.

  • Data Analysis
    Data Analysis is the process of inspecting, cleansing, and modeling data to discover useful information. This skill is important for roles like data scientist and business analyst, as it drives informed decision-making and strategy development.
  • Machine Learning
    Machine Learning is the ability to develop algorithms that enable computers to learn from data. This skill is important for data scientists and AI engineers to create predictive models and enhance automation.
  • Data Visualization
    Data Visualization is the ability to represent data graphically, making complex information accessible and understandable. this skill is important for analysts and decision-makers to identify trends, insights, and patterns effectively.
  • Exploratory Data Analysis
    Exploratory Data Analysis involves analyzing datasets to summarize their main characteristics. This skill is important for data scientists and analysts to uncover insights, identify patterns, and inform decision-making.
  • Data Wrangling
    Data Wrangling is the process of cleaning, transforming, and organizing raw data into a usable format. This skill is important for data analysts and scientists to derive insights effectively.
  • Programming Languages
    Programming Languages are formal systems used to instruct computers. This skill is important for software developers, data analysts, and engineers to create efficient, functional applications.

What Your Team Will Achieve After This Training

  • Utilize Python programming for data analysis, increasing efficiency and innovation in data-driven projects
  • Apply advanced analytical techniques to solve complex business problems, enhancing decision-making processes
  • Develop predictive models to forecast future trends, enabling proactive decision-making and competitive advantage
  • Create compelling data visualizations to communicate findings effectively to stakeholders, facilitating informed decision-making
  • Develop and deploy scalable data pipelines and workflows, automating repetitive tasks and streamlining data processing tasks for increased productivity

Topics & Program Outline

The curriculum is organized into focused modules built by industry experts and delivered virtually or on-premise. Interactive sessions reflect the evolving demands of the workplace, keeping the learning both relevant and practical.

  1. Introduction to data science
    • Definition and importance
    • Key concepts and skills
  2. Analytics landscape
    • Overview of analytics types
    • Role in decision-making
    • Impact on various industries
  3. Life cycle of a data science project
    • Stages and milestones
    • Project management techniques
    • Common challenges and solutions
  4. Data science tools & technologies
    • Overview of software and languages
    • Comparison of data science platforms
    • Introduction to cloud computing for data science
  1. Measures of central tendency
    • Calculating mean, median, and mode
    • Applications in data analysis
    • Limitations and considerations
  2. Measures of dispersion
    • Understanding range, variance, and standard deviation
    • Importance in data interpretation
    • Calculating and comparing dispersion measures
  3. Descriptive statistics
    • Role in data summarization
    • Visualization techniques
  4. Probability basics
    • Fundamental concepts and rules
    • Applications in decision-making
  5. Marginal probability
    • Definition and calculation
    • Examples and applications
    • Differences from conditional probability
  6. Bayes theorem
    • Explanation and formula
    • Bayesian vs. frequentist approaches
  7. Probability distributions
    • Types and characteristics
    • Selecting appropriate distributions
    • Modeling real-world phenomena
  8. Hypothesis testing
    • Formulating and testing hypotheses
    • Error types and power of a test
  1. Install Anaconda
    • Step-by-step installation guide
    • Configuring environments
    • Troubleshooting common issues
  2. Data types & variables
    • Overview of Python data types
    • Using variables effectively
    • Type conversion and manipulation
  3. String & regular expressions
    • Basics of string manipulation
    • Introduction to regular expressions
  1. Python list
    • Creating and manipulating lists
    • List comprehensions and operations
  2. Python dictionaries
    • Dictionary basics and usage
    • Advanced techniques and patterns
  3. Python set
    • Set operations
    • Comparing sets and lists
    • Efficiency and applications
  4. Python tuple
    • Understanding tuples and their usage
    • Tuple operations and methods
    • Applications in data science
  5. Comprehensions
    • List, set, and dictionary comprehensions
    • Writing efficient and readable code
  1. For loop
    • Basics and syntax
    • Advanced for loop patterns
    • Nested loops and iterations
  2. While loop
    • Understanding while loops
    • Controlling loop execution
  3. Break statement
    • Controlling flow in loops
    • Scenarios and examples
  4. Next statements
    • Using next for iteration control
    • Differences from break
  5. Repeat statement
    • Introduction and usage
    • Repeat vs. while loops
    • Applications in data manipulation
  6. If, if…else statements
    • Making decisions in Python
    • Complex conditional structures
  7. Switch statement
    • Simulating switch-case in Python
    • Alternatives and approaches
  1. Writing your functions (UDF)
    • Defining custom functions
    • Parameters and return values
  2. Calling Python functions
    • Basic and advanced calling techniques
    • Handling arguments and return values
    • Debugging and troubleshooting
  3. Functions with arguments
    • Understanding arguments and parameters
    • Keyword vs. positional arguments
    • Advanced argument techniques
  4. Calling Python functions by passing arguments
    • Effective argument passing
    • Using *args and **kwargs
  5. Lambda functions
    • Introduction to lambda expressions
    • Comparing lambda with regular functions
  6. Classes & objects
    • Basics of object-oriented programming
    • Defining classes and creating instances
    • Advanced OOP concepts and patterns
  1. Reading files with Python
    • Opening and reading text files
    • Handling CSV and Excel files
  2. Writing files from Python
    • Writing to text and CSV files
    • Generating Excel files programmatically
    • Ensuring data integrity and formatting
  3. Reading files using Pandas library
    • Introduction to Pandas for file input
    • Reading various file formats
    • Dataframe operations for data ingestion
  4. Saving data using Pandas library
    • Exporting data to different formats
    • Customizing output parameters
    • Efficient data storage techniques
  1. Clean & prepare datasets
    • Identifying and handling missing data
    • Data type conversions and normalization
    • Feature engineering and preparation
  2. Manipulate DataFrame
    • Advanced DataFrame manipulation techniques
    • Merging, joining, and concatenating data
    • Aggregation and grouping for analysis
  3. Summarize data
    • Generating summary statistics
    • Understanding data through descriptive analytics
    • Visual summary techniques
  4. Churn insights from data
    • Pattern recognition and anomaly detection
    • Insight generation methodologies
  1. Charts using Matplotlib
    • Basics of plotting with Matplotlib
    • Customizing plots and charts
    • Complex visualizations and layouts
  2. Charts using Seaborn
    • Introduction to Seaborn for statistical plots
    • Advanced data visualization techniques
  3. Charts using ggplot
    • ggplot basics and philosophy
    • Building plots layer by layer
    • Comparing ggplot with Matplotlib and Seaborn
  1. ANOVA
    • Understanding analysis of variance
    • Conducting ANOVA tests
    • Interpreting results for decision-making
  2. Linear regression (OLS)
    • Building linear models with OLS
    • Diagnostics and assumptions testing
  3. Principal component analysis
    • Dimensionality reduction techniques
    • PCA for feature extraction
    • Visualization and interpretation of components
  4. Factor analysis
    • Basics and methodology of factor analysis
    • Application in uncovering latent variables
    • Factor extraction and rotation techniques
  5. Logistic regression (MLE)
    • Using logistic regression for classification
    • Model building and evaluation
  6. K-nearest neighbor algorithm
    • Understanding KNN and its applications
    • Choosing the right value of K
    • Scaling and preprocessing for KNN
  7. Decision tree
    • Constructing decision trees for classification and regression
    • Tree pruning and complexity control
    • Visualizing and interpreting decision trees
  1. Understand time series data
    • Characteristics of time series data
    • Decomposition of time series
    • Seasonality and trend analysis
  2. Visualizing time series components
    • Time series visualization techniques
    • Identifying patterns and cycles
    • Tools and libraries for time series visualization
  3. Exponential smoothing
    • Simple and double exponential smoothing models
    • Tuning and optimization of parameters
    • Forecasting with exponential smoothing
  4. Holt's model
    • Introduction to Holt's linear trend method
    • Application in trend analysis
    • Combining level and trend components
  5. Holt-Winter's model
    • Capturing seasonality with Holt-Winters
    • Parameter selection and model fitting
  6. ARIMA
    • Basics of ARIMA modeling
    • Model identification and fitting
    • Diagnostics and forecasting with ARIMA
  1. What is machine learning?
    • Defining machine learning and its scope
    • Types of machine learning models
    • Applications and examples in the industry
  2. Supervised learning
    • Overview of supervised learning techniques
    • Building and evaluating models
  3. Unsupervised learning
    • Exploring unsupervised learning algorithms
    • Clustering and dimensionality reduction
  4. Using Scikit-learn
    • Introduction to Scikit-learn for machine learning
    • Preprocessing and model selection
    • Building pipelines and model evaluation
  5. Scikit-learn classes
    • Understanding the Scikit-learn API
    • Popular classes and their applications
    • Advanced techniques and customizations

Who Should Attend?

This program suits professionals at many levels across the organization, including:

  • Data Analysts
  • Business Analysts
  • Software Developers
  • Operations Analysts
  • Data Engineers
  • Research Analysts
  • Financial Analysts
  • Marketing Analysts
  • Healthcare Analysts
  • IT Specialists
  • Market Researchers
  • Managers

What are the Prerequisites?

Professionals with a basic understanding of computer operations, such as file management and software installation, and familiarity with fundamental concepts of mathematics, especially algebra and arithmetic operations, can take the Introduction to Data Science training course.

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Off-site: focused, instructor-led group learning away from everyday workplace distractions.

We host your teams at a venue of your preferred choice
Built-in group activities for bonding
Full uninterrupted schedule for focus/retention
Boosts morale and signals commitment
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        What Sets Edstellar Apart

        Experienced Trainers

        Our trainers are drawn from a vetted global network and bring years of industry expertise, keeping every session practical and impactful.

        Proven Quality

        With a strong global track record, Edstellar is known for quality and engaging delivery.

        Industry-Relevant Curriculum

        Our programs are built by experts to match the demands of today's industry.

        Fully Customizable

        Every program can be tailored to your organization's goals.

        Comprehensive Support

        We provide pre- and post-session support for a complete learning experience.

        Global Multi-Location & Multilingual Training Delivery

        We deliver in multiple languages to support diverse global teams.

        Hear from Organizations We've Trained

        "The Introduction to Data Science training provided me with comprehensive capabilities that elevated my expertise. As a Senior Database Administrator, I needed to understand strategic frameworks deeply, and this course workshops gave me hands-on experience with industry best practices. My productivity and technical capabilities have increased dramatically since applying these concepts. Highly recommend for anyone serious about this field.”

        Ashley Stevens

        Senior Database Administrator,

        Advanced Analytics Platform Provider

        "The Introduction to Data Science training provided critical insights into practical applications that enhanced my consulting capabilities. As a Lead Data Scientist, I now leverage interactive labs with expertise to The practical exercises on real-world case studies prepared me perfectly for real-world client scenarios. We've reduced implementation timelines by 45% on comparable projects, demonstrating immediate value from this investment.”

        Marcus Olsen

        Lead Data Scientist,

        Predictive Modeling Solutions Firm

        "As a Senior Data Engineer overseeing operational excellence initiatives, the Introduction to Data Science training significantly elevated our team's capabilities. The course expertly covered industry best our operational effectiveness. We completed our comprehensive digital transformation initiative significantly ahead of schedule. Our department has achieved remarkable improvements, demonstrating this course's lasting organizational impact.”

        Rami Tariq

        Senior Data Engineer,

        Data-Driven Insights Company

        “Edstellar’s IT & Technical training programs have been instrumental in strengthening our engineering teams and building future-ready capabilities. The hands-on approach, practical cloud scenarios, and expert guidance helped our teams improve technical depth, problem-solving skills, and execution across multiple projects. We’re excited to extend more of these impactful programs to other business units.”

        Aditi Rao

        L&D Head,

        A Global Technology Company

        Recognition That Motivates Your Team

        Upon successful completion of the training course offered by Edstellar, employees receive a course completion certificate, symbolizing their dedication to ongoing learning and professional development.

        This certificate validates the employee's acquired skills and is a powerful motivator, inspiring them to enhance their expertise further and contribute effectively to organizational success.

        Recognition That Motivates Your Team
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