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Curriculum Designed by Experts
DOWNLOAD CURRICULUMThe Data Science Corporate Training Course by Multisoft Virtual Academy equips professionals with the essential skills to analyze and interpret complex data. Designed for corporate teams, this comprehensive program covers key concepts such as data visualization, statistical modeling, machine learning, and predictive analytics. Participants will gain hands-on experience with popular tools like Python, R, and SQL, ensuring practical knowledge for real-world applications. Tailored to organizational needs, the course enhances data-driven decision-making and problem-solving abilities. Delivered by industry experts, this training fosters collaboration and innovation, empowering teams to leverage data for business growth and competitive advantage.
Through expert-led instruction, hands-on projects, and real-world applications, learners will develop the skills necessary to collect, analyze, interpret, and derive actionable insights from complex datasets.
- Introduction to Data Science
- Analytics Landscape
- Life Cycle of a Data Science Projects
- Data Science Tools & Technologies
- Measures of Central Tendency
- Measures of Dispersion
- Descriptive Statistics
- Probability Basics
- Marginal Probability
- Bayes Theorem
- Probability Distributions
- Hypothesis Testing
- Install Anaconda
- Data Types & Variables
- String & Regular Expressions
- Python list
- Python dictionaries
- Python set
- Python tuple
- Comprehensions
- For Loop
- While Loop
- Break Statement
- Next Statements
- Repeat Statement
- if, if…else Statements
- Switch Statement
- Writing your own functions (UDF)
- Calling Python Functions
- Functions with Arguments
- Calling Python Functions by passing Arguments
- Lambda Functions
- Classes & Objects
- Reading files with Python
- Writing files from Python
- Reading files using Pandas library
- Saving Data using Pandas library
- Clean & Prepare Datasets
- Manipulate DataFrame
- Summarize Data
- Churn Insights from Data
- Charts using Matplotlib
- Charts using Seaborn
- Charts using ggplot
- ANOVA
- Linear Regression (OLS)
- Case Study: Linear Regression
- Principal Component Analysis
- Factor Analysis
- Case Study: PCA/FA
- Logistic Regression (MLE)
- Case Study: Logistic Regression
- K-Nearest Neighbor Algorithm
- Case Study: K-Nearest Neighbor Algorithm
- Decision Tree
- Case Study: Decision Tree
- Understand Time Series Data
- Visualizing Time Series Components
- Exponential Smoothing
- Holt's Model
- Holt-Winter's Model
- ARIMA
- Case Study: Time Series Modeling on Stock Price
- What is Machine Learning?
- Supervised Learning
- Unsupervised Learning
- Using Scikit-learn
- Scikit-learn classes
- Case Study: Machine Learning Algorithm
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Customized trainings to understand specific project requirements
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