Start a Career in High-Demand Data Analytics
With Data Analytics Course in Kochi
Expert Training
Learn from industry professionals with real-world experience in data analytics.
Comprehensive Curriculum
Gain in-depth knowledge of analytics tools, techniques, and best practices.
100% Placement Assistance
Secure your dream job with our robust placement support.
Hands-On Offline Classes
Experience interactive, in-person learning for a better understanding of concepts.
Empower Your Career with Data Analytics Skills
Are you aspiring to excel in the high-demand field of Data Analytics? STC Technologies’ Data Analytics Course in Kochi is your gateway to mastering one of the most essential skills of the digital era. Our comprehensive program is designed to equip you with a solid foundation in data analytics principles, tools, and techniques, helping you unlock exciting career opportunities in the IT sector.
We offer a top-notch Data Analytics course in Kochi with 100% placement assistance. Our offline classes provide an interactive and hands-on learning experience, ensuring you gain practical skills. Choosing the right Data Analytics training institute is your first step toward securing high-paying jobs in the IT industry.
Course Details
Duration– 3 Months Offline classroom
Syllabus
- Introduction to Data Analytics
- What is Data Analytics?
- Importance and applications of Data Analytics
- Types of Data Analytics (Descriptive, Diagnostic, Predictive, and Prescriptive)
- The data analytics lifecycle
- Principles of Data Visualization
- Understanding Data and Insights
- Types of Data Visualizations and Their Use
- Tools for Data Visualization
- Excel, Power BI, Tableau for Visualization
- Design Best Practices
- Storytelling With Data
- Effective Dashboards and Reports
- Interactive Visualizations
- Embedding Visualizations into Websites
- Interactive Dashboards (PowerBI/Tableau)
- Introduction to Python Programming
- Python Basics: Variables, Data Types, Operators
- Control Structures: Conditional Statements, Loops
- Data Structures and Functions
- Lists,Tuples, Dictionaries, Sets
- Functions and Lambda Expressions
- Libraries for Data Analytics
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Matplotlib and Seaborn for Visualization
- Working with Files
- Handling CSV, Excel Files
- Introduction to Data Analysis with Python
- Data Cleaning and Transformation
- Exploratory Data Analysis (EDA)
- Basic Machine Learning with Python
- Supervised vs Unsupervised Learning
- Data Understanding
- Data Cleaning
- Descriptive Statistics
- Data Visualization
- Correlation Analysis
- Feature Engineering
- Outlier Detection
- Distribution Analysis
- Hypothesis Testing Data Insights and Business Understanding
- Data Collection Methods
- Surveys,Interviews,Observations
- Data Quality and Integrity
- EnsuringAccuracy, Completeness, and Consistency
- Handling Missing Data
- Data Collection Tools
- Data Exploration
- Descriptive Statistics (mean, median, mode, standard deviation) Data Distribution (histograms, box plots)
- Handling Missing Data
- Imputation (mean, median, mode)
- Removal of Missing Data
- Forward and Backward Filling
- Handling Outliers
- Identifying Outliers (IQR, Z-score method)
- Treating Outliers (removal, transformation)
- Data Transformation
- Normalization and Standardization
- Log Transformation
- Scaling Techniques (Min-Max Scaling, Z-Score Scaling)
- Feature Engineering
- Creating New Features from Existing Data
- Feature Encoding (One-Hot Encoding, Label Encoding) Polynomial Features
- Data Integration
- Merging Data from Multiple Sources
- Joining and Concatenation Data Type Conversion
- Converting Categorical to Numerical Data
- Handling Date and Time Variables
- Dealing with Duplicates
- Identifying and Removing Duplicates
- Data Quality Assessment
- Ensuring Consistency, Completeness, and Accuracy
- Linear Algebra
- Calculus
- Probability Theory
- Discrete Mathematics
- Descriptive Statistics
- Probability Distributions
- Regression Analysis Correlation and Covariance
- Statistical Modeling and Evaluation
- K-Means Clustering
- Decision Trees
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Logistic Regression
- Random Forest
- Linear Regression
- Introduction to Machine Learning
- Types of Machine Learning
- Supervised Learning Algorithms
- LinearRegression, LogisticRegression
- Decision Trees, Random Forests
- K-Nearest Neighbors (k-NN), Support Vector Machines (SVM)
- Unsupervised Learning Algorithms
- Clustering:K-Means, Hierarchical Clustering
- Dimensionality Reduction: PCA
- Model Evaluation
- Cross-Validation, Bias-Variance Tradeoff
- Performance Metrics: Accuracy, Precision, Recall, F1 Score
- Introduction to Databases
- SELECTQueries, WHERE Clause, Sorting Data
- Advanced SQLQueries
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
- Grouping and Aggregations: GROUP BY, HAVING
- Subqueries and NestedQueries
- Using Subqueries for Data Extraction
- SQL Functions and Operators
- String Functions, Date Functions, Mathematical Functions
- Database Design and Normalization
- Understanding Keys: Primary, Foreign, Composite
- Normal Forms and Data Integrity
- SQL Performance Optimization Indexing
- Query Optimization Techniques
- Basic Formulas
- Advanced Formulas
- Pivot Tables
- Introduction to PowerBI
- Overview and Setup
- Data Loading and Data Types
- Data Transformation with PowerQuery
- Importing Data from Multiple Sources
- Cleaning and Shaping Data
- CreatingRelationships
- Creating InteractiveReports and Dashboards
- Visualizations: Bar Charts, Line Graphs, Maps, etc.
- Slicers and Filters
- Drill-through Reports
- DAX (Data Analysis Expressions)
- Basic DAX Formulas
- Calculated Columns and Measures
- Time Intelligence in DAX
- Power BI Service
- Publishing Reports and Sharing Dashboards
- Collaborating and Sharing Insights
- Power BI for Business Insights Creating KPI Dashboards
- Real-time Analytics
- Introduction to Tableau
- Installation and InterfaceOverview
- Connecting to Data Sources
- Data Preparation andTransformation
- Data Cleaning and Shaping in Tableau
- Handling Missing Data
- Building Basic Visualizations
- Types of Charts: Bar, Line, Pie, Scatter Plots, etc.
- Filters, Parameters, and Sets
- Dashboards in Tableau
- Creating Interactive Dashboards
- Actions and Navigation in Dashboards
- Sharing and Publishing Tableau Workbooks
- Tableau Public and Tableau Server/Online
- Introduction to Deep Learning
- Overview of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)
- Difference between Traditional Machine Learning and Deep Learning
- Applications of Deep Learning (e.g., image recognition, natural language processing, autonomous driving)
- Neural Networks Fundamentals Wht is a Neural Network?
- Structure of a Neural Network: Neurons, Layers (Input, Hidden, Output)
- Activation Functions (Sigmoid, ReLU, Tanh, Softmax)
- Feedforward Neural Networks (FNN)
- Training a Neural Network: Forward Propagation and Backpropagation
- Types of Neural Networks
- Perceptron: Basic neural network structure
- Multilayer Perceptron (MLP): Deep neural networks with multiple layers
- Convolutional Neural Networks (CNN): Image processing and computer vision
- Recurrent Neural Networks (RNN): Sequence data processing (time-series, text, speech)
- Long Short-Term Memory (LSTM): Advanced RNNs for handling long-term dependencies
- Generative Adversarial Networks (GAN): Generating new data through adversarial training
- Training Deep Learning Models
- Data Preprocessing: Normalization, Scaling, Data Augmentation
- Splitting Data: Training, Validation, and Test Sets
- Cross-Validation and Hyperparameter Tuning
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
- Deep Learning Frameworks and Libraries
- Introduction to Popular Deep Learning Libraries: TensorFlow, Keras, PyTorch
- How to use these libraries for building and training models
- Implementing a simple neural network with Keras or TensorFlow
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Top recommended courses based on Industry demand and recent Placement statistics.
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Testimonials
What our students say
, IBS Software,
" STC teaches according to the current industry standards. They are helping even after course completion until we get a satisfied job. If you wish to have a carrier in software testing or software training, then choosing STC to learn will be a good opinion you can take ".
, Qa lead analyst at IQVIA,
" It was a great pleasure being with STC. Excellent skills and high quality professionalism of this team had helped me to get software testing job. STC is actually a good institute to learn different software training courses. "
, TCS , Infopark
" The assistance and support I received was great. They had helped me in all aspects and had given guidance until I get a satisfied job. Being with STC had helped me to get this bright future."
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