Python with Data Science and Machine Learning
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: $1,520.00 | Duration: 2 weeks | 40 hours
Full tuition is due at the time of enrollment
- Python for data science and machine learning
- Implement machine learning algorithms
- NumPy, Pandas, Matplotlib, Seaborn for data analysis and visualization
- Plotly for interactive dynamic visualizations
- SciKit-Learn for machine learning tasks
- K-Means Clustering, Logistic Regression, Linear Regression, Random Forest and Decision Trees
- Natural Language Processing and Spam Filters, Neural Networks
- Introduction to Data Science. What is Data Science? Why Python for data science?
- How leading companies are harnessing the power of Data Science with Python? Different phases of a typical Analytics/Data Science project and role of python. Anaconda vs. Python
- Overview of Python- Starting with Python. Introduction to installation of Python. Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…). Understand Jupyter notebook & Customize Settings.
- Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) Installing & loading Packages & Name Spaces. Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries). List and Dictionary Comprehensions
- Variable & Value Labels – Date & Time Values. Basic Operations – Mathematical – string – date Reading and writing data. Simple plotting
- Control flow & conditional statements. Debugging & Code profiling.
- SCIENTIFIC DISTRIBUTIONS USED IN PYTHON FOR DATA SCIENCE – Numpy, scify, pandas, scikitlearn, statmodels, nltk etc.
- ACCESSING/IMPORTING AND EXPORTING DATA USING PYTHON MODULES – Importing Data from various sources (Csv, txt, excel, access etc). Database Input (Connecting to database)
- Viewing Data objects – subsetting, methods. Exporting Data to various formats. Important python modules: Pandas, beautifulsoup
- DATA MANIPULATION – CLEANSING – MUNGING USING PYTHON MODULE – Cleansing Data with Python. Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc).
- Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc). Python Built-in Functions (Text, numeric, date, utility functions).
- Python User Defined Functions. Stripping out extraneous information. Normalizing data. Formatting data. Important Python modules for data manipulation (Pandas, Numpy, re, math,string, datetime etc)
- DATA ANALYSIS – VISUALIZATION USING PYTHON- Introduction exploratory data analysis. Descriptive statistics, Frequency Tables and summarization.
- Univariate Analysis (Distribution of data & Graphical Analysis). Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis).
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc). Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy. stats etc)
- BASIC STATISTICS & IMPLEMENTATION OF STATS METHODS IN PYTHON – 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 Chisquare. 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
Data Science with R , Automation Testing with Python
Is this course of interest to anyone?
This course may be an interest to:
- Those who want to pursue a career in data science within the shortest length of time
- Data analysts interested in transitioning to Python and SQL
- Those who are good at math and statistics
How do I qualify?
To enroll in this Data Science and Machine Learning Bootcamp, you must have a High School or GED equivalent. Knowledge of basic computer functions and applications is required.