Data Science & Analytics
Unlock the power of data with our comprehensive training & Learn to turn raw information into valuable insights and make data-driven decisions like a pro.
About this Program
In the subject of data science, complex data sets are mined for insights using mathematical, statistical, and programming methods. Businesses today have access to a wealth of data, and data science aids in making sense of this data by offering useful insights. These insights can be applied to boost customer satisfaction, optimize marketing initiatives, and enhance corporate operations. Data science is now a vital tool for businesses in a variety of industries due to the growing significance of data-driven decision-making!
Modules of Curiculum
- Overview of data science and Analytics
- Applications and real-world examples
- Introduction to tools: Jupyter Notebook, Anaconda distribution
- Introduction to Python programming
- Essential libraries: NumPy, Pandas, Matplotlib
- Hands-on exercise: Data manipulation and analysis using Pandas
- Understanding data types and data cleaning
- Descriptive statistics and data visualization techniques with Matplotlib and Seaborn
- Hands-on exercise: Exploring and visualizing a dataset
- Introduction to Probability and Statistics
- Hypothesis testing and confidence intervals
- Hands-on exercise: Statistical analysis using Python
- Introduction to Supervised and unsupervised learning
- Linear regression, logistic regression, decision trees
- Hands-on exercise: Building and evaluating regression and classification models
- Ensemble methods: Random Forests and Gradient Boosting
- Dimensionality reduction: Principal Component Analysis (PCA)
- Hands-on exercise: Applying advanced machine learning algorithms
- Neural networks and activation functions
- Deep learning frameworks: TensorFlow or PyTorch
- Hands-on exercise: Building a basic neural network
- Introduction to text data preprocessing
- Text classification using Naive Bayes, sentiment analysis
- Hands-on exercise: NLP tasks with Python libraries like NLTK or spaCy
- Introduction to distributed computing and Apache Spark
- Processing large datasets with Spark
- Hands-on exercise: Analyzing big data using Spark
- Feature engineering techniques: encoding, scaling, feature selection
- Model evaluation metrics and techniques
- Hands-on exercise: Improving models through feature engineering and evaluation
- Introduction to time series data
- Techniques for time series forecasting
- Hands-on exercise: Analysing and forecasting time series data
- Introduction to the capstone project
- Defining the problem statement and dataset
- Project example: Predicting customer churn or stock price prediction
What you'll learn
Who Can Enroll
Frequently asked questions
You will learn concepts and techniques such as data manipulation, exploratory data analysis, statistical modeling, machine learning algorithms, data visualization, feature engineering, model evaluation, ensemble methods, deep learning, and real-world project implementation. You will also receive guidance and support from experienced mentors throughout the program.
Handy knowledge of Python will help but not mandatory. No prior knowledge of programming or statistics required , suitable for beginners.
It typically range from 90 days to 105 days.
Once you successfully complete the entire course, you will get a certificate of completion. This certificate will help you will build your credibility as a Data Scientist or a Data Engineer.
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