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Introduction to Data Science Training

Drive Team Excellence with Introduction to Data Science Corporate Training

Empower your teams with expert-led on-site/in-house or virtual/online Introduction to Data Science Training through Edstellar, a premier Introduction to Data Science training company for organizations globally. Our customized training program equips your employees with the skills, knowledge, and cutting-edge tools needed for success. Designed to meet your specific training needs, this Introduction to Data Science group training program ensures your team is primed to drive your business goals. Transform your workforce into a beacon of productivity and efficiency.

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.

Key Skills Employees Gain from Introduction to Data Science Training

Introduction to Data Science skills corporate training will enable teams to effectively apply their learnings at work.

  • Data Analysis
  • Machine Learning
  • Data Visualization
  • Exploratory Data Analysis
  • Data Wrangling
  • Programming Languages

Key Learning Outcomes of Introduction to Data Science Training Workshop

Edstellar’s Introduction to Data Science group training will not only help your teams to acquire fundamental skills but also attain invaluable learning outcomes, enhancing their proficiency and enabling application of knowledge in a professional environment. By completing our Introduction to Data Science workshop, teams will to master essential Introduction to Data Science and also focus on introducing key concepts and principles related to Introduction to Data Science at work.


Employees who complete Introduction to Data Science training will be able to:

  • 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

Key Benefits of the Introduction to Data Science Group Training

Attending our Introduction to Data Science classes tailored for corporations offers numerous advantages. Through our Introduction to Data Science group training classes, participants will gain confidence and comprehensive insights, enhance their skills, and gain a deeper understanding of Introduction to Data Science.

  • Develop skills in statistical analysis and probability, enabling you to interpret data accurately and solve real-world problems
  • Equip professionals with the knowledge of machine learning algorithms to make data-driven decisions and accurately predict future trends
  • Gain practical experience in time series forecasting, opening new perspectives on how to predict and react effectively to future data trends
  • Master the use of advanced data manipulation techniques with Pandas, enhancing the team's ability to clean and prepare data for analysis
  • Explore the full lifecycle of data science projects, from initial data collection to final presentation, ensuring a comprehensive understanding of the field

Topics and Outline of Introduction to Data Science Training

Our virtual and on-premise Introduction to Data Science training curriculum is divided into multiple modules designed by industry experts. This Introduction to Data Science training for organizations provides an interactive learning experience focused on the dynamic demands of the field, making it 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

Target Audience for Introduction to Data Science Training Course

The Introduction to Data Science training program can also be taken by professionals at various levels in the organization.

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

Prerequisites for Introduction to Data Science Training

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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At Edstellar, we understand the importance of impactful and engaging training for employees. To ensure the training is more interactive, we offer Face-to-Face onsite/in-house or virtual/online for companies. This approach has proven to be effective, outcome-oriented, and produces a well-rounded training experience for your teams.

 Virtual trainig

Our virtual group training sessions bring expert-led, high-quality training to your teams anywhere, ensuring consistency and seamless integration into their schedules.

With global reach, your employees can get trained from various locations
The consistent training quality ensures uniform learning outcomes
Participants can attend training in their own space without the need for traveling
Organizations can scale learning by accommodating large groups of participants
Interactive tools can be used to enhance learning engagement
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Edstellar's onsite group training delivers immersive and insightful learning experiences right in the comfort of your office.

Higher engagement and better learning experience through face-to-face interaction
Workplace environment can be tailored to learning requirements
Team collaboration and knowledge sharing improves training effectiveness
Demonstration of processes for hands-on learning and better understanding
Participants can get their doubts clarified and gain valuable insights through direct interaction
 Off-site trainig

Edstellar's off-site group training programs offer a unique opportunity for teams to immerse themselves in focused and dynamic learning environments away from their usual workplace distractions.

Distraction-free environment improves learning engagement
Team bonding can be improved through activities
Dedicated schedule for training away from office set up can improve learning effectiveness
Boosts employee morale and reflects organization's commitment to employee development

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        Get Your Team Members Recognized with Edstellar’s Course Certificate

        Upon successful completion of the Introduction to Data Science 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.

        Course Completion Certificate

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        The instructor-led training is conducted by certified trainers with extensive expertise in the field. Participants will benefit from the instructor's vast knowledge, gaining valuable insights and practical skills essential for success in Access practices.

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