Data Science with Python
Live Online | Instructor-Led | Hands-on Training
Analyze data with Python’s data science libraries to automate everyday tasks such as automating the aggregation, updating, and formatting of data, and read and write complex database queries. You will learn Python from the basics all the way to creating your own apps. Beginning with the basics of Python syntax, students will move on to more advanced topics such as dictionaries, custom functions, and sorting algorithms.
Get a head start on your data science career and Python engineering career with this beginner-friendly program. It’s no surprise that data science is one of today’s best and highest-paying jobs.
- Career in a High-Demand Profession: There is still a shortage of Data Scientists while demand is high. Data science is experiencing strong growth in the U.S. and the Bureau of Labor Statistics predicts an increase in jobs of about 28% through 2026. Having said this, the risk of losing a career by doing nothing in matters of data science may be relatively low in the long run, especially if you broaden the scope of the field to include related positions like research engineers or machine learning engineers.
- Learn From Experts: The instructors have been carefully selected based on their educational background, relevant work experience, and teaching abilities. We have highly qualified trainers with at least ten to twelve years of teaching experience in the industry. Students with good feedback are also kept on our faculty.
- Prepare for Your New Career: You will gain valuable skills in data science through this certificate program. Develop your portfolio by completing real-world projects, while receiving support from your one-on-one mentor to help you with your job search, resume, and career development.
Flexible scheduling | Weekdays | Weekends
Tuition: $3,860.00 | Duration: 5 weeks | 100 hours
Tuition: $3,960.00 | Duration: 6 weeks | 120 hours (includes Python certification)
Full tuition is due at the time of enrollment
- SQL Fundamentals
- Python
- Data Science with Python
SQL Fundamentals
- DDL Statements – CREATE, DROP, ALTER, etc.
- DML Statements – UPDATE, RENAME, DELETE, TRUNCATE, ALTER, MODIFY, etc,
- DQL Statement – SELECT
- Restricting and Sorting Data
- Date and Time
- Join, Merge Tables
- VIEWS, INDEX, Stored Procedures, Managing synonyms and sequences.
Python
- Introduction to Python
- Basic Python Syntax
- Loops
- Collections
- Functions
- Keywords and Optional. Parameters
- Modules
- Exceptions
- Input and Output
- Simple Character Matches
- Comprehensions
- Iterators and generators
- Decorators
- Context managers
- Descriptors
- Metaclasses
- Conventions
- Proxy
- Constructors
Data Science with Python
- Introduction to Data Science
- Anaconda vs. Python
- Packages/Libraries -(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
- Variable & Value Labels
- Control flow & conditional statements
- Debugging & Code profiling
- Viewing Data objects
- Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc). Python Built-in Functions (Text, numeric, date, utility functions).
- Python User Defined Functions.
- Data Analysis
- Univariate Analysis (Distribution of data & Graphical Analysis).
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis).
- Creating Graphs
- Basic Statistics – Measures of Central Tendencies and Variance.
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem. Inferential Statistics -Sampling – Concept of Hypothesis Testing.
- Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square. Important modules for statistical methods: Numpy, Scipy, Pandas
- PYTHON: MACHINE LEARNING -PREDICTIVE MODELING – BASICS. Introduction to Machine Learning & Predictive Modeling. Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning. Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation).
- Overfitting (Bias-Variance Trade off) & Performance Metrics. Feature engineering & dimension reduction.
- Concept of optimization & cost function. Concept of gradient descent algorithm. Concept of Cross validation (Bootstrapping, K-Fold validation etc).
- Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)
- MACHINE LEARNING ALGORITHMS & APPLICATIONS – IMPLEMENTATION IN PYTHON. Linear & Logistic Regression.
- Segmentation – Cluster Analysis (K-Means). Decision Trees (CART/CD 5.0). Ensemble Learning (Random Forest, Bagging & boosting).
- Artificial Neural Networks(ANN). Support Vector Machines(SVM). Other Techniques (KNN, Naïve Bayes, PCA). Introduction to Text Mining using NLTK.
- Introduction to Time Series Forecasting (Decomposition & ARIMA). Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc). Fine tuning the models using Hyper parameters, grid search, piping etc.
Data collection specialist, Data Scientist, Data Analyst, Data Engineer
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