Datascience training in New York

Enter a The 100 Hours boot camp will guide you starting from how to code to make end to end A.I applicationshort description of the course.

STUDENTS ENROLLED

    By the end of this course, students will be able to:

    ● Learn Python programming and Advance Python
    ● Collect, extract, query, clean, and aggregate data for analysis ​‣
    ● Perform visual and statistical analysis on data using Python and its associated libraries and Tools
    ● Build, implement, and evaluate data science problems using appropriate machine learning
    ● models and algorithms
    ● Use appropriate data visualization to communicate findings
    ● Create clear and reproducible reports to stakeholders
    ● Identify big data problems and articulate how distributed systems and parallel computing
    ● technologies are solving these challenges
    ● Apply question, modeling, and validation problem-solving processes to datasets from various industries in order to provide insight into real-world problems and solutions
    ● Create Deep learning model for real world problem

    40 hours of real time project in Healthcare, Finance,CRM and Sales  domain

    Course Curriculum

    Learn Python from Basic to Advance
    installation 00:00:00
    Fundamentals of Python ,Numpy and Pandas
    Python and Numpy 00:00:00
    Python & NumPy​ ​Demonstrate introductory programming concepts using Python and NumPy as a tool to navigate data sources and collections 00:00:00
    UNIX​ ​Utilize UNIX commands to navigate file systems and modify files 00:00:00
    Descriptive Statistics​ ​Define and apply descriptive statistical fundamentals to sample data sets Intro to Plotting and Visualization 00:00:00
    Git Hub:git​ ​Learn to keep track of changes and iterations using git version control from your terminal 00:00:00
    Intro to Plotting and Visualization Practice plotting and visualizing data using Python libraries like matplotlib and Seaborn ​ Visualization 00:00:00
    EXPLORATORY DATA ​ ​ ANALYSIS
    Project 3​ ​Using a provided dataset, students will explore, clean, and model data, outlining their strategy and explaining their results 00:00:00
    Experiment Design​ ​Plan experimental study design with a well thought out problem statement and data framework Pandas & Pivot Tables ​Use Pandas to read, clean, parse, and plot data using functions such as boolean, indexing, math series, joins, and others 00:00:00
    SciPy & Statsmodels​ ​Review statistical testing concepts (p-values, confidence intervals, lambda functions, correlation/causation) with SciPy and Stats models 00:00:00
    Web Scraping​ ​Learn to scrape website data using popular scraping tools 00:00:00
    Bootstrapping​ ​Practice resampling and building inferences about your data 00:00:00
    CLASSICAL STATISTICAL MODELING
    Project 3​ ​Using a provided dataset, students will explore, clean, and model data, outlining their strategy and explaining their results 00:00:00
    Linear &, logistic regression Use scikit learn and statsmodels to run linear and logistic regression models and learn to evaluate model fit 00:00:00
    Bias-Variance Tradeoff​ ​Articulate the bias-variance trade-off as you practice evaluating classical statistical models 00:00:00
    Gradient Descent​ ​Dive into the math and theory of how gradient descent helps to optimize loss function for regression models 00:00:00
    Feature Selection​ ​Use feature selection to deepen your knowledge of study design and model evaluation 00:00:00
    Regularization & Optimization Learn to apply regularization and optimization when evaluating model fit 00:00:00
    K-Nearest Neighbors​ ​Begin to look at classification models through an application of the kNN algorithm 00:00:00
    MACHINE LEARNING MODELS
    Project 4​ ​Clustering ​Students will scrape and model their own data using multiple methods, outlining their approach and evaluating any risks or limitations 00:00:00
    Define clustering and it’s advantages and disadvantages from classification models 00:00:00
    Ensemble Models​ ​Build and evaluate ensemble models, using decision trees, random forests, bagging, and boosting 00:00:00
    NLP​ ​Get introduced to natural language processing through sentiment analysis of scraped website data. 00:00:00
    Naive Bayes​ ​Learn how Naive Bayes can simplify the process of analyzing data for supervised learning algorithms 00:00:00
    Time Series Analysis ​Analyze and model time series data using the ARIMA model in Pandas 00:00:00
    PCA and Dimension Reduction with practical example on handwriting digits 00:00:00
    SVM algorithm on regression and Classification problem with hyper parameter tuning 00:00:00
    Understand Eigen Values and Eigen Vectors 00:00:00
    Introduction to Deep Learning
    Artificial Neural Network concept ,design and implementation 00:00:00
    Feed Forward Neural Network and Back Propagation 00:00:00
    Apply Neural Network to solve Employee Retention Dataset 00:00:00
    Deep Dive into Artificial Inteligence
    CNN Introduction,Building Fundamentals of how CNN works 00:00:00
    OpenCV for Face Recognition and Video Analytics 00:00:00
    Recurrent Neural Network and LSTM model with CNN for large text Data Training and learning pattern 00:00:00
    Recommender Systems​ ​Build and apply basic recommender systems in order to predict on sample user data 00:00:00
    Portfolio Development
    ​Work with career coaches to create and polish your portfolio for employers 00:00:00
    Interview Prep​ ​Practice 20+ data science case studies to prep for job interviews 00:00:00

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