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About Data Science
Data Science: Transforming Complex Data into Competitive Advantage and Growth
Data Science is a multidisciplinary field that combines statistical analysis, machine learning, and domain expertise to extract valuable insights from data. It has become a cornerstone of modern enterprise strategy, enabling organizations to harness the power of data for smarter decisions and innovation. Here are some key aspects of Data Science.
Data-Driven Insights, Predictive Analytics, Scalability, Performance Optimization, Automation, Real-Time Decision-Making, Cross-Platform Integration.
Overall, Data Science is a powerful and versatile discipline that enables organizations to uncover hidden patterns, optimize operations, and gain a competitive edge in today’s fast-paced, data-rich environment.
- AI & Data Innovation Hub
- Project-Driven Learning Tracks
- Machine Learning Mastery
- Hands-On Data Lab Access
- Global Certification Programs
- Career-Focused Skill Building
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Why Choose Tironation
Data Manipulation
Machine Learning with Python
Data Manipulation with Python – Pandas
Understanding Pandas
Defining Data Structures
Data Operations (filtering, sorting, grouping, aggregation, merging) and Data Standardization
Pandas: File Read and Write Support
Week 1
- Exploring and Understanding Data
- Exploring Numeric Variables
- Understanding Types of Data
- Qualitative and Quantitative Analysis
- Studying Descriptive Statistics
- Exploring Numeric Variables
- Central tendancy – The Model
- Measuring Spread Variance & Standard Deviation
- Visualising Numeric Variables – Box-plots & Histograms
Week 2
- Understanding Numeric Data,Uniform & Normal Distributions
- Measuring the Central Tendency – The Mode
- Exploring Relationships between Variables
- Visualizing Relationships – Scatterplots
- Nominal and Ordinal Measurement
- Interval and Ratio Measurement
- Statistical Investigation
- Inferential Statistics
- Probability & Central Limit Theorem
Week 3
- Exploratory Data Analysis
- Normal Distribution
- Distance Measures
- Euclidean & Manhattan Distance
- Minkowski
- Cosine, Correlation
- Importance of Hypothesis Testing in Business
- Null and Alternate Hypothesis
- Understanding Types of Errors
Week 4
- Contingency Table and Decision Making
- Confidence Coefficient
- Upper Tail Test
- Understanding Parametric Tests
- Z-Test
- Degree of Freedom
- One-Way ANOVA Test
- F-Distribution
- Chi-Square Test
Week 1
- Regression Methods for Forecasting Numeric Data
- Understanding Neural Networks
- From Biological to Artificial Neurons
- Activation Functions
- Deep Learning – Neural Networks and Support Vector Machines
- What is Regression?
- Model Selection
- Generalized Regression
- Simple Linear Regression
Week 2
- Multiple Linear Regression
- Correlations
- Correlation between X and Y
- Ridge and Regularized Regression
- LASSO, TimeSeries
- Prediction: TimeDependent/Variant Data
- Ordinary Least Square Regression Model
- Dummy Variable Regression Model
- Interaction Regression Model
- Non-Linear Regression Model
Week 3
- Perform RegressionAnalysis with Multiple Variables
- Network Topology
- Recurrent and Gaussian Neural Network
- The Number of Layers
- The Direction of Information Travel
- The Number of Nodes in Each Layer
- Training Neural Networks with Backpropagation
- Support Vector Machines
- Classification with Hyperplanes
- Finding the Maximum Margin
Week 4
- The Case of Linearly Separable Data
- The Case of Non-Linearly Separable Data
- Retrieve Data using SQL Statements
- Using Kernels for Non-Linear Spaces
- Classification
- K-NN, Naïve Bayes, Support Vector Machines
- Defining Classification
- Understanding Classification and Prediction
- Decision Tree Classifier
- How to Build DecisionTrees?
Week 5
- Basic Algorithmfor a Decision Tree
- Decision Trees and Data Mining
- Random Forest Classifier
- Features of Random Forests
- Out of Box Error Estimate
- Variable Importance
- Naive Bayes Classifier Model
- Bayesian Theorem
- Advantages and Disadvantages of Naive Bayes
- Understanding Support Vector Machines
- Understanding Linear SVMs
- Logistic Regression
- Bagging and Boosting (Adab Oost)
Week 1
- Understanding K-means Clustering
- K-means and PseudoCode
- K-means Clustering using R
- TF-IDF and Cosine Similarity
- Application to VectorSpace Model
- What is Hierarchical Clustering?
- Hierarchical Clustering Algorithm
- Understanding Agglomerative Clustering
Week 2
- DBSCAN Clustering
- What is Association Rule Mining?
- Association Rule Strength Measures
- Checking Apriori Algorithms
- Ordering Items
- Understanding Candidate Generation
- Performing Visualization
- Dimensionality reduction
Why Choose Tironation
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Senior ManagerChoosing Tironation's IT course was the best investment in my career. The comprehensive curriculum, combined with hands-on labs and projects, allowed me to gain practical experience and develop the skills needed to excel in the fast-paced IT industry.
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Senior ManagerI can recommend Tironation's IT course highly enough. The curriculum is up-to-date with industry trends, the instructors are passionate about what they teach, and the supportive learning environment fosters growth and innovation
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Senior ManagerCourse stands out for its blend of theoretical knowledge and practical application. The instructors' expertise and the hands-on projects enhanced my technical skills and how to approach problems creatively and collaboratively."
