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Best Data Science Course Training in San Francisco

Data Science Using Python and R Programming offers the best in industry data science program which is a potent culmination of best in class instructors, groundbreaking course material and an AI-powered LMS platform – AISPRY.
  • Accredited by The State University of New York (SUNY)
  • 184 Hours of Intensive Live Online Sessions
  • 2 Capstone Live Projects
  • Job Placement Assistance
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"With hundreds of companies hiring for the role of Data Scientist, 12 million new jobs will be created in the field of Data Science by the year 2026. " - (Source). From the last four years, the Data Scientist job is ranked as number one in U.S by Glassdoor. As per the reports of U.S. Bureau of Labor Statistics, the demand for data science skills will bring a 27.8percent rise in employment by 2026. The demand for Data Scientists is astonishing and greater, but there is a lack of professional Data Scientists. Data science, Research, and analytics are now available to businesses with the aid of automation and education. Many training programs are being conducted to provide Data Scientists to the business. Data Science is widening and is being adopted by many companies to gain a competitive edge and generate revenue by improving production. To be at the forefront in this data-driven world, industries require professional Data Scientists with strong technical skills.

Data Science

data science course duration in San Fransisco - 360digitmg

Total Duration

4 Months

data science course pre-requisites in San Fransisco - 360digitmg

Prerequisites

  • Computer Skills
  • Basic Mathematical Concepts
  • Analytical Mindset

Data Science Training Program Overview

The Data Science Using Python and R is a truly transformative program which takes the students from novices to job-ready candidates in this competitive US market. The defining feature of this program that it aims to blend scientifically rigorous and multidisciplinary knowledge into easily understandable concepts. This course aims to be a primer and will attract learners from diverse domains such as Business Professionals, Programmers, Researchers, Academia, Industry practitioners among others. Students will begin by learning the basics of statistics and slowly progress to learning more complex algorithms in the data science toolkit such as regression, tree-based methods, supervised and unsupervised algorithms.

Data Science Courses Learning Outcomes

Data Science course using R and Python training in the San Francisco is designed to build the workforce what the current market needs. This course helps students and professionals to acquire the knowledge of Data Science and all the technical skills required. 360DigiTMG offers a Data Science course in San Franchisco which focusses to train the aspirants with industry use cases so that the students can gain in-depth knowledge of the applications of tools. Data Scientist job is considered as the sexiest job of this century, this course enables students to grab lucrative jobs and achieve their goals. The course includes basics and advanced versions of Data Science. The training is delivered by industry experts who have exceptional experience and a team of dedicated mentors who guide students throughout their learning journey.

Work with various data generation sources
Perform Text Mining to generate Customer Sentiment Analysis
Analyse structured and unstructured data using different tools and techniques
Develop an understanding of Descriptive and Predictive Analytics
Apply Data-driven, Machine Learning approaches for business decisions
Build models for day-to-day applicability
Perform Forecasting to take proactive business decisions
Use Data Concepts to represent data for easy understanding

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184 hours

Live Online Sessions

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150+ hours

Assignments

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120 hours

Live Projects

Who Should Sign Up?

  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Banking and Finance Analysts
  • Marketing Managers
  • Supply Chain Professionals
  • HR Managers
  • Math, Science and Commerce Graduates

Syllabus of Data Science Training in San Francisco

This Data Science Program follows the CRISP-ML(Q) Methodology. The premier modules are devoted to a foundational perspective of Statistics, Mathematics, Business Intelligence, and Exploratory Data Analysis. The successive modules deal with Probability Distribution, Hypothesis Testing, Data Mining Supervised, Predictive Modelling - Multiple Linear Regression, Lasso And Ridge Regression, Logistic Regression, Multinomial Regression, and Ordinal Regression. Later modules deal with Data Mining Unsupervised Learning, Recommendation Engines, Network Analytics, Machine Learning, Decision Tree and Random Forest, Text Mining, and Natural Language Processing. The final modules deal with Machine Learning - classifier techniques, Perceptron, Multilayer Perceptron, Neural Networks, Deep Learning Black-Box Techniques, SVM, Forecasting, and Time Series algorithms. This is the most enriching training program in terms of the array of topics covered.

  • Introduction to Python Programming
  • Installation of Python & Associated Packages
  • Graphical User Interface
  • Installation of Anaconda Python
  • Setting Up Python Environment
  • Data Types
  • Operators in Python
  • Arithmetic operators
  • Relational operators
  • Logical operators
  • Assignment operators
  • Bitwise operators
  • Membership operators
  • Identity operators
  • Check out the Top Python Programming Interview Questions and Answers here.
  • Data structures
    • Vectors
    • Matrix
    • Arrays
    • Lists
    • Tuple
    • Sets
    • String Representation
    • Arithmetic Operators
    • Boolean Values
    • Dictionary
  • Conditional Statements
    • if statement
    • if - else statement
    • if - elif statement
    • Nest if-else
    • Multiple if
    • Switch
  • Loops
    • While loop
    • For loop
    • Range()
    • Iterator and generator Introduction
    • For – else
    • Break
  • Functions
    • Purpose of a function
    • Defining a function
    • Calling a function
    • Function parameter passing
    • Formal arguments
    • Actual arguments
    • Positional arguments
    • Keyword arguments
    • Variable arguments
    • Variable keyword arguments
    • Use-Case *args, **kwargs
  • Function call stack
    • Locals()
    • Globals()
  • Stackframe
  • Modules
    • Python Code Files
    • Importing functions from another file
    • __name__: Preventing unwanted code execution
    • Importing from a folder
    • Folders Vs Packages
    • __init__.py
    • Namespace
    • __all__
    • Import *
    • Recursive imports
  • File Handling
  • Exception Handling
  • Regular expressions
  • Oops concepts
  • Classes and Objects
  • Inheritance and Polymorphism
  • Multi-Threading
  • What is a Database
  • Types of Databases
  • DBMS vs RDBMS
  • DBMS Architecture
  • Normalisation & Denormalization
  • Install PostgreSQL
  • Install MySQL
  • Data Models
  • DBMS Language
  • ACID Properties in DBMS
  • What is SQL
  • SQL Data Types
  • SQL commands
  • SQL Operators
  • SQL Keys
  • SQL Joins
  • GROUP BY, HAVING, ORDER BY
  • Subqueries with select, insert, update, delete statements
  • Views in SQL
  • SQL Set Operations and Types
  • SQL functions
  • SQL Triggers
  • Introduction to NoSQL Concepts
  • SQL vs NoSQL
  • Database connection SQL to Python
  • Check out the SQL for Data Science One Step Solution for Beginners here.

Learn about insights on how data is assisting organizations to make informed data-driven decisions. Gathering the details about the problem statement would be the first step of the project. Learn the know-how of the Business understanding stage. Deep dive into the finer aspects of the management methodology to learn about objectives, constraints, success criteria, and the project charter. The essential task of understanding business Data and its characteristics is to help you plan for the upcoming stages of development. Check out the CRISP - Business Understanding here.

  • All About 360DigiTMG & Innodatatics Inc., USA
  • Dos and Don'ts as a participant
  • Introduction to Big Data Analytics
  • Data and its uses – a case study (Grocery store)
  • Interactive marketing using data & IoT – A case study
  • Course outline, road map, and takeaways from the course
  • Stages of Analytics - Descriptive, Predictive, Prescriptive, etc.
  • Cross-Industry Standard Process for Data Mining
  • Typecasting
  • Handling Duplicates
  • Outlier Analysis/Treatment
    • Winsorization
    • Trimming
    • Local Outlier Factor
    • Isolation Forests
  • Zero or Near Zero Variance Features
  • Missing Values
    • Imputation (Mean, Median, Mode, Hot Deck)
    • Time Series Imputation Techniques
      • 1) Last Observation Carried Forward (LOCF)
      • 2) Next Observation Carried Backward (NOCB)
      • 3) Rolling Statistics
      • 4) Interpolation
  • Discretization / Binning / Grouping
  • Encoding: Dummy Variable Creation
  • Transformation
    • Transformation - Box-Cox, Yeo-Johnson
  • Scaling: Standardization / Normalization
  • Imbalanced Handling
    • SMOTE
    • MSMOTE
    • Undersampling
    • Oversampling

In this module, you will learn about dealing with the Data after the Collection. Learn to extract meaningful information about Data by performing Uni-variate analysis which is the preliminary step to churn the data. The task is also called Descriptive Analytics or also known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information along with Visualizations to show the information in graphs/plots

  • Machine Learning project management methodology
  • Data Collection - Surveys and Design of Experiments
  • Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
  • Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
  • Balanced versus Imbalanced datasets
  • Cross Sectional versus Time Series vs Panel / Longitudinal Data
    • Time Series - Resampling
  • Batch Processing vs Real Time Processing
  • Structured versus Unstructured vs Semi-Structured Data
  • Big vs Not-Big Data
  • Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
  • Sampling techniques for handling Balanced vs. Imbalanced Datasets
  • What is the Sampling Funnel and its application and its components?
    • Population
    • Sampling frame
    • Simple random sampling
    • Sample
  • Measures of Central Tendency & Dispersion
    • Population
    • Mean/Average, Median, Mode
    • Variance, Standard Deviation, Range

The raw Data collected from different sources may have different formats, values, shapes, or characteristics. Cleansing, or Data Preparation, Data Munging, Data Wrapping, etc., are the next steps in the Data handling stage. The objective of this stage is to transform the Data into an easily consumable format for the next stages of development.

  • Feature Engineering on Numeric / Non-numeric Data
  • Feature Extraction
  • Feature Selection
    • Forward Feature Selection
    • Backward Feature Selection
    • Exhaustive Feature Selection
    • Recursive feature elimination (RFE)
    • Chi-square Test
    • Information Gain
  • What is Power BI?
    • Power BI Tips and Tricks & ChatGPT Prompts
    • Overview of Power BI
    • Architecture of PowerBI
    • PowerBI and Plans
    • Installation and introduction to PowerBI
  • Transforming Data using Power BI Desktop
    • Importing data
    • Changing Database
    • Data Types in PowerBI
    • Basic Transformations
    • Managing Query Groups
    • Splitting Columns
    • Changing Data Types
    • Working with Dates
    • Removing and Reordering Columns
    • Conditional Columns
    • Custom columns
    • Connecting to Files in a Folder
    • Merge Queries
    • Query Dependency View
    • Transforming Less Structured Data
    • Query Parameters
    • Column profiling
    • Query Performance Analytics
    • M-Language

Learn the preliminaries of the Mathematical / Statistical concepts which are the foundation of techniques used for churning the Data. You will revise the primary academic concepts of foundational mathematics and Linear Algebra basics. In this module, you will understand the importance of Data Optimization concepts in Machine Learning development. Check out the Mathematical Foundations here.

  • Data Optimization
  • Derivatives
  • Linear Algebra
  • Matrix Operations

Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data. In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. In hierarchical clustering, K means clustering is the most used clustering algorithm. Understand the different mathematical approaches to perform data segregation. Also, learn about variations in K-means clustering like K-medoids, and K-mode techniques, and learn to handle large data sets using the CLARA technique.

  • Clustering 101
  • Distance Metrics
  • Hierarchical Clustering
  • Non-Hierarchical Clustering
  • DBSCAN
  • Clustering Evaluation metrics

Dimension Reduction (PCA and SVD) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.

  • Prinicipal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)

Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence, and Lift used to define the rules with the help of the Apriori algorithm. Learn the pros and cons of each of the metrics used in Association rules.

  • Association rules mining 101
  • Measurement Metrics
  • Support
  • Confidence
  • Lift
  • User Based Collaborative Filtering
  • Similarity Metrics
  • Item Based Collaborative Filtering
  • Search Based Methods
  • SVD Method

The study of a network with quantifiable values is known as network analytics. The vertex and edge are the nodes and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

  • Entities of a Network
  • Properties of the Components of a Network
  • Measure the value of a Network
  • Community Detection Algorithms

Learn to analyse unstructured textual data to derive meaningful insights. Understand the language quirks to perform data cleansing, extract features using a bag of words and construct the key-value pair matrix called DTM. Learn to understand the sentiment of customers from their feedback to take appropriate actions. Advanced concepts of text mining will also be discussed which help to interpret the context of the raw text data. Topic models using LDA algorithm, emotion mining using lexicons are discussed as part of NLP module.

  • Sources of data
  • Bag of words
  • Pre-processing, corpus Document Term Matrix (DTM) & TDM
  • Word Clouds
  • Corpus-level word clouds
  • Sentiment Analysis
  • Positive Word clouds
  • Negative word clouds
  • Unigram, Bigram, Trigram
  • Semantic network
  • Extract, user reviews of the product/services from Amazon and tweets from Twitter
  • Install Libraries from Shell
  • Extraction and text analytics in Python
  • LDA / Latent Dirichlet Allocation
  • Topic Modelling
  • Sentiment Extraction
  • Lexicons & Emotion Mining
  • Check out the Text Mining Interview Questions and Answers here.
  • Machine Learning primer
  • Difference between Regression and Classification
  • Evaluation Strategies
  • Hyper Parameters
  • Metrics
  • Overfitting and Underfitting

Revise Bayes theorem to develop a classification technique for Machine learning. In this tutorial, you will learn about joint probability and its applications. Learn how to predict whether an incoming email is spam or a ham email. Learn about Bayesian probability and its applications in solving complex business problems.

  • Probability – Recap
  • Bayes Rule
  • Naïve Bayes Classifier
  • Text Classification using Naive Bayes
  • Checking for Underfitting and Overfitting in Naive Bayes
  • Generalization and Regulation Techniques to avoid overfitting in Naive Bayes
  • Check out the Naive Bayes Algorithm here.

k Nearest Neighbor algorithm is a distance-based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The KNN Classifier also known as a lazy learner is a very popular algorithm and one of the easiest for application.

  • Deciding the K value
  • Thumb rule in choosing the K value.
  • Building a KNN model by splitting the data
  • Checking for Underfitting and Overfitting in KNN
  • Generalization and Regulation Techniques to avoid overfitting in KNN

In this tutorial, you will learn in detail about the continuous probability distribution. Understand the properties of a continuous random variable and its distribution under normal conditions. To identify the properties of a continuous random variable, statisticians have defined a variable as a standard, learning the properties of the standard variable and its distribution. You will learn to check if a continuous random variable is following normal distribution using a normal Q-Q plot. Learn the science behind the estimation of value for a population using sample data.

  • Probability & Probability Distribution
  • Continuous Probability Distribution / Probability Density Function
  • Discrete Probability Distribution / Probability Mass Function
  • Normal Distribution
  • Standard Normal Distribution / Z distribution
  • Z scores and the Z table
  • QQ Plot / Quantile - Quantile plot
  • Sampling Variation
  • Central Limit Theorem
  • Sample size calculator
  • Confidence interval - concept
  • Confidence interval with sigma
  • T-distribution Table / Student's-t distribution / T table
  • Confidence interval
  • Population parameter with Standard deviation known
  • Population parameter with Standard deviation not known

Learn to frame business statements by making assumptions. Understand how to perform testing of these assumptions to make decisions for business problems. Learn about different types of Hypothesis testing and its statistics. You will learn the different conditions of the Hypothesis table, namely Null Hypothesis, Alternative hypothesis, Type I error, and Type II error. The prerequisites for conducting a Hypothesis test, and interpretation of the results will be discussed in this module.

  • Formulating a Hypothesis
  • Choosing Null and Alternative Hypotheses
  • Type I or Alpha Error and Type II or Beta Error
  • Confidence Level, Significance Level, Power of Test
  • Comparative study of sample proportions using Hypothesis testing
  • 2 Sample t-test
  • ANOVA
  • 2 Proportion test
  • Chi-Square test

Data Mining supervised learning is all about making predictions for an unknown dependent variable using mathematical equations explaining the relationship with independent variables. Revisit the school math with the equation of a straight line. Learn about the components of Linear Regression with the equation of the regression line. Get introduced to Linear Regression analysis with a use case for the prediction of a continuous dependent variable. Understand about ordinary least squares technique.

  • Scatter diagram
  • Correlation analysis
  • Correlation coefficient
  • Ordinary least squares
  • Principles of regression
  • Simple Linear Regression
  • Exponential Regression, Logarithmic Regression, Quadratic or Polynomial Regression
  • Confidence Interval versus Prediction Interval
  • Heteroscedasticity / Equal Variance
  • Check out the Linear Regression Interview Questions and Answers here.

In the continuation of the Regression analysis study, you will learn how to deal with multiple independent variables affecting the dependent variable. Learn about the conditions and assumptions to perform linear regression analysis and the workarounds used to follow the conditions. Understand the steps required to perform the evaluation of the model and to improvise the prediction accuracies. You will be introduced to concepts of variance and bias.

  • LINE assumption
  • Linearity
  • Independence
  • Normality
  • Equal Variance / Homoscedasticity
  • Collinearity (Variance Inflation Factor)
  • Multiple Linear Regression
  • Model Quality metrics
  • Deletion Diagnostics
  • Check out the Linear Regression Interview Questions here.

You have learned about predicting a continuous dependent variable. As part of this module, you will continue to learn Regression techniques applied to predict attribute Data. Learn about the principles of the logistic regression model, understand the sigmoid curve, and the usage of cut-off value to interpret the probable outcome of the logistic regression model. Learn about the confusion matrix and its parameters to evaluate the outcome of the prediction model. Also, learn about maximum likelihood estimation.

  • Principles of Logistic regression
  • Types of Logistic regression
  • Assumption & Steps in Logistic regression
  • Analysis of Simple logistic regression results
  • Multiple Logistic regression
  • Confusion matrix
  • False Positive, False Negative
  • True Positive, True Negative
  • Sensitivity, Recall, Specificity, F1
  • Receiver operating characteristics curve (ROC curve)
  • Precision Recall (P-R) curve
  • Lift charts and Gain charts
  • Check out the Logistic Regression Interview Questions and Answers here.

Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques of Lasso and Ridge techniques are discussed in this module.

Extension to logistic regression We have multinomial and Ordinal Logistic regression techniques used to predict multiple categorical outcomes. Understand the concept of multi-logit equations, baseline, and making classifications using probability outcomes. Learn about handling multiple categories in output variables including nominal as well as ordinal data.

  • Logit and Log-Likelihood
  • Category Baselining
  • Modeling Nominal categorical data
  • Handling Ordinal Categorical Data
  • Interpreting the results of coefficient values

As part of this module, you learn further different regression techniques used for predicting discrete data. These regression techniques are used to analyze the numeric data known as count data. Based on the discrete probability distributions namely Poisson, negative binomial distribution the regression models try to fit the data to these distributions. Alternatively, when excessive zeros exist in the dependent variable, zero-inflated models are preferred, you will learn the types of zero-inflated models used to fit excessive zeros data.

  • Poisson Regression
  • Poisson Regression with Offset
  • Negative Binomial Regression
  • Treatment of data with Excessive Zeros
  • Zero-inflated Poisson
  • Zero-inflated Negative Binomial
  • Hurdle Model

Support Vector Machines / Large-Margin / Max-Margin Classifier

  • Hyperplanes
  • Best Fit "boundary"
  • Linear Support Vector Machine using Maximum Margin
  • SVM for Noisy Data
  • Non- Linear Space Classification
  • Non-Linear Kernel Tricks
  • Linear Kernel
  • Polynomial
  • Sigmoid
  • Gaussian RBF
  • SVM for Multi-Class Classification
  • One vs. All
  • One vs. One
  • Directed Acyclic Graph (DAG) SVM

Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing the duration of time before the event. Real-time applications of survival analysis in customer churn, medical sciences, and other sectors are discussed as part of this module. Learn how survival analysis techniques can be used to understand the effect of the features on the event using the Kaplan-Meier survival plot.

  • Examples of Survival Analysis
  • Time to event
  • Censoring
  • Survival, Hazard, and Cumulative Hazard Functions
  • Introduction to Parametric and non-parametric functions

Decision Tree models are some of the most powerful classifier algorithms based on classification rules. In this tutorial, you will learn about deriving the rules for classifying the dependent variable by constructing the best tree using statistical measures to capture the information from each of the attributes.

  • Elements of classification tree - Root node, Child Node, Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information gain
  • Decision Tree C5.0 and understanding various arguments
  • Checking for Underfitting and Overfitting in Decision Tree
  • Pruning – Pre and Post Prune techniques
  • Generalization and Regulation Techniques to avoid overfitting in Decision Tree
  • Random Forest and understanding various arguments
  • Checking for Underfitting and Overfitting in Random Forest
  • Generalization and Regulation Techniques to avoid overfitting in Random Forest
  • Check out the Decision Tree Questions here.

Learn about improving the reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go-to techniques in ensemble techniques. The parallel and sequential approaches taken in Bagging and Boosting methods are discussed in this module. Random forest is yet another ensemble technique constructed using multiple Decision trees and the outcome is drawn from the aggregating the results obtained from these combinations of trees. The Boosting algorithms AdaBoost and Extreme Gradient Boosting are discussed as part of this continuation module. You will also learn about stacking methods. Learn about these algorithms which are providing unprecedented accuracy and helping many aspiring data scientists win first place in various competitions such as Kaggle, CrowdAnalytix, etc.

  • Overfitting
  • Underfitting
  • Voting
  • Stacking
  • Bagging
  • Random Forest
  • Boosting
  • AdaBoost / Adaptive Boosting Algorithm
  • Checking for Underfitting and Overfitting in AdaBoost
  • Generalization and Regulation Techniques to avoid overfitting in AdaBoost
  • Gradient Boosting Algorithm
  • Checking for Underfitting and Overfitting in Gradient Boosting
  • Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting
  • Extreme Gradient Boosting (XGB) Algorithm
  • Checking for Underfitting and Overfitting in XGB
  • Generalization and Regulation Techniques to avoid overfitting in XGB
  • Check out the Ensemble Techniques Interview Questions here.

Time series analysis is performed on the data which is collected with respect to time. The response variable is affected by time. Understand the time series components, Level, Trend, Seasonality, Noise, and methods to identify them in a time series data. The different forecasting methods available to handle the estimation of the response variable based on the condition of whether the past is equal to the future or not will be introduced in this module. In this first module of forecasting, you will learn the application of Model-based forecasting techniques.

  • Introduction to time series data
  • Steps to forecasting
  • Components to time series data
  • Scatter plot and Time Plot
  • Lag Plot
  • ACF - Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naïve forecast methods
  • Errors in the forecast and it metrics - ME, MAD, MSE, RMSE, MPE, MAPE
  • Model-Based approaches
  • Linear Model
  • Exponential Model
  • Quadratic Model
  • Additive Seasonality
  • Multiplicative Seasonality
  • Model-Based approaches Continued
  • AR (Auto-Regressive) model for errors
  • Random walk
  • Check out the Time Series Interview Questions here.

In this continuation module of forecasting learn about data-driven forecasting techniques. Learn about ARMA and ARIMA models which combine model-based and data-driven techniques. Understand the smoothing techniques and variations of these techniques. Get introduced to the concept of de-trending and de-seasonalize the data to make it stationary. You will learn about seasonal index calculations which are used to re-seasonalize the result obtained by smoothing models.

  • ARMA (Auto-Regressive Moving Average), Order p and q
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d, and q
  • ARIMA, ARIMAX, SARIMAX
  • AutoTS, AutoARIMA
  • A data-driven approach to forecasting
  • Smoothing techniques
  • Moving Average
  • Exponential Smoothing
  • Holt's / Double Exponential Smoothing
  • Winters / Holt-Winters
  • De-seasoning and de-trending
  • Seasonal Indexes
  • RNN, Bidirectional RNN, Deep Bidirectional RNN
  • Transformers for Forecasting
  • N-BEATS, N-BEATSx
  • N-HiTS
  • TFT - Temporal Fusion Transformer

The Perceptron Algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.

  • Neurons of a Biological Brain
  • Artificial Neuron
  • Perceptron
  • Perceptron Algorithm
  • Use case to classify a linearly separable data
  • Multilayer Perceptron to handle non-linear data

Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a Artificial Neural Network.

  • Integration functions
  • Activation functions
  • Weights
  • Bias
  • Learning Rate (eta) - Shrinking Learning Rate, Decay Parameters
  • Error functions - Entropy, Binary Cross Entropy, Categorical Cross Entropy, KL Divergence, etc.
  • Artificial Neural Networks
  • ANN Structure
  • Error Surface
  • Gradient Descent Algorithm
  • Backward Propagation
  • Network Topology
  • Principles of Gradient Descent (Manual Calculation)
  • Learning Rate (eta)
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Minibatch Stochastic Gradient Descent
  • Optimization Methods: Adagrad, Adadelta, RMSprop, Adam
  • Convolution Neural Network (CNN)
  • ImageNet Challenge – Winning Architectures
  • Parameter Explosion with MLPs
  • Convolution Networks
  • Recurrent Neural Network
  • Language Models
  • Traditional Language Model
  • Disadvantages of MLP
  • Back Propagation Through Time
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Network (GRU)
  • Sequence 2 Sequence Models
  • Transformers
  • Generative AI
  • ChatGPT
  • DALL-E-2
  • Mid Journey
  • Crayon
  • What Is Prompt Engineering?
  • Understanding Prompts: Inputs, Outputs, and Parameters
  • Crafting Simple Prompts: Techniques and Best Practices
  • Evaluating and Refining Prompts: An Iterative Process
  • Role Prompting and Nested Prompts
  • Chain-of-Thought Prompting
  • Multilingual and Multimodal Prompt Engineering
  • Generating Ideas Using "Chaos Prompting"
  • Using Prompt Compression

SUNY University Syllabus

  • Data Engineering, Machine Learning, & AWS
  • Amazon S3 Simple Storage Service
  • Data Movement
  • Data Pipelines & Workflows
  • Jupyter Notebook & Python
  • Data Analysis Fundamentals
  • Athena, QuickSight, & EMR
  • Feature Engineering Overview
  • Problem Framing & Algorithm Selection
  • Machine Learning in SageMaker
  • ML Algorithms in SageMaker
  • Advanced SageMaker Functionality
  • AI/ML Services
  • Problem Formulation & Data Collection
  • Data Preparation & SageMaker Security
  • Model Training & Evaluation
  • AI Services & SageMaker Applications
  • Machine Learning
  • Machine Learning Services
  • Machine Learning Regression Models
  • Machine Learning Classification Models
  • Machine Learning Clustering Models
  • Project Jupyter & Notebooks
  • Azure Machine Learning Workspaces
  • Azure Data Platform Services
  • Azure Storage Accounts
  • Storage Strategy
  • Azure Data Factory
  • Non-relational Data Stores
  • Machine Learning Data Stores & Compute
  • Machine Learning Orchestration & Deployment
  • Model Features & Differential Privacy
  • Machine Learning Model Monitoring
  • Azure Data Storage Monitoring
  • Data Process Monitoring
  • Data Solution Optimization
  • High Availability & Disaster Recovery
  • Certificate Course in Data Science by SUNY
Tools Covered
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Course Fee Details

Virtual Classroom Training
Mode of training: Live Online
  • 10+ hours of live online doubt clarification sessions
  • Free access to USD 500 worth study materials - mindmaps, digital book on Data Science & many more
  • Blockchain security enabled tamper-proof certificate(s)
  • Free Learning Management System Access
  • Real-life industry-based projects with AiSPRY

Next Batch: 21st July 2024

USD 894

2055 Learners

339 Reviews

Self-Paced learning
Mode of training: Self-Paced Learning
  • Free access to USD 500 worth study materials - mindmaps, digital book on Data Science & many more
  • Blockchain security enabled tamper-proof certificate(s)
  • Free Learning Management System Access
  • Real-life industry-based projects with AiSPRY

Next Batch: 21st July 2024

USD 813

2055 Learners

339 Reviews

Payment Accepted

Why Choose 360DigiTMG for Data Science Training Institute?
data science certification in San Francisco - 360digitmg
data science certification in San Francisco - 360digitmg

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Data Science Certification Panel of Coaches

Artificial Intelligence & Deep Learning Course Training -360digitmg

Bharani Kumar Depuru

  • Areas of expertise: Data analytics, Digital Transformation, Industrial Revolution 4.0
  • Over 18+ years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Deloitte, Hewlett Packard Enterprise, Amazon, Tech Mahindra, Cummins, Accenture, IBM
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
Read More >
 
Artificial Intelligence & Deep Learning Course Training -360digitmg

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data
  • Trained over 1,500 professional across 12 countries
  • Worked as a Data scientist for 18+ years across several industry domains
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
Read More >
 
Artificial Intelligence & Deep Learning Course Training - 360digitmg

Bhargavi Kandukuri

  • Areas of expertise: Business analytics, Quality management, Data visualisation with Tableau, COBOL, CICS, DB2 and JCL
  • Electronics and communications engineer with over 19+ years of industry experience
  • Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
  • Trained over 750+ professionals across the globe in three years
  • Worked with Infosys Technologies, iGate, Patni Global Solutions as technology analyst
Read More >
 
data science using python and r certification - 360digitmg
data science using python and r certification - 360digitmg

Certificate

Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Data Science Certificate is your passport to an accelerated career path.

Alumni Speak

Nur Fatin

"Coming from a psychology background, I was looking for a Data Science certification that can add value to my degree. The 360DigiTMG program has such depth, comprehensiveness, and thoroughness in preparing students that also looks into the applied side of Data Science."

"I'm happy to inform you that after 4 months of enrolling in a Professional Diploma in Full Stack Data Science, I have been offered a position that looks into applied aspects of Data Science and psychology."

Nur Fatin

Associate Data Scientist

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Thanujah Muniandy

"360DigiTMG has an outstanding team of educators; who supported and inspired me throughout my Data Science course. Though I came from a statistical background, they've helped me master the programming skills necessary for a Data Science job. The career services team supported my job search and, I received two excellent job offers. This program pushes you to the next level. It is the most rewarding time and money investment I've made-absolutely worth it.”

Thanujah Muniandy

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Ann Nee, Wong

"360DigiTMG’s Full Stack Data Science programme equips its graduates with the latest skillset and technology in becoming an industry-ready Data Scientist. Thanks to this programme, I have made a successful transition from a non-IT background into a career in Data Science and Analytics. For those who are still considering, be bold and take the first step into a domain that is filled with growth and opportunities.”

Ann Nee, Wong

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Mohd Basri

"360DigiTMG is such a great place to enhance IR 4.0 related skills. The best instructor, online study platform with keen attention to all the details. As a non-IT background student, I am happy to have a helpful team to assist me through the course until I have completed it.”

Mohd Basri

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Ashner Novilla

"I think the Full Stack Data Science Course overall was great. It helped me formalize and think more deeply about ways to tackle the projects from a Data Science perspective. Also, I was remarkably impressed with the instructors, specifically their ability to make complicated concepts seem very simple."

"The instructors from 360DigiTMG were great and it showed how they engaged with all the students even in a virtual setting. Additionally, all of them are willing to help students even if they are falling behind. Overall, a great class with great instructors. I will recommend this to upcoming deal professionals going forward.”

Ashner Novilla

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Our Alumni Work At

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FAQs for Data Science Course Training

There are numerous jobs available for data science professionals. Once you finish the training, assignments and the live projects successfully, we will circulate your resume to the organizations with whom we have formal agreements on job placements. We also conduct regular webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.

There is a huge disparity in how these terms are used, sometimes DS, DA and BA are used interchangeably. Although, the gap is narrowing now, BA is strictly dealing with advanced analytics but DS is more about bringing predictive power using machine learning techniques. One thing is clear, Data Modelling typically means designing the scheman etc. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organization.

The US market is currently going through an unprecedented economy and the job growth has also been the best in recent times. Multiple reputed sources are documenting the acute shortage of data science professionals. Our program aims to address this by preparing the candidates not only by providing theoretical concepts, but helping them learn by doing. You will also greatly benefit from doing a Live project through Innodatatics, a leading Data Analytics company which will prepare you in dealing with implementing a data science project end-to-end.

It has been well documented that there is a startling shortage of data science professionals worldwide and for the US market in particular. Now the onus is on you, the candidate and if you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.

 

To help you achieve that 360DigiTMG provides internship opportunities through Innodatatics, our SAN FRANSISCO-based consulting partner, for deserving participants to help them gain real-life experience. You will be involved in executing a project end to end and this will help you with gaining the job training to help you in this career path.

The data science profession has given rise to a multitude of sub-domains although most of the responsibilities overlap there are subtle and pertinent differences in each of the roles. See below for a short description of what each of the roles represents. Please be wary that depending on the organizational structure and the industry, the roles may have different meaning but this should serve as a basic guideline.

 

A Data Analyst is tasked with Data Cleansing, Exploratory Data Analysis, and Data Visualization, among other functions. These responsibilities pertain more to the use and analysis of historical data for understanding the current state. So simply put, a Data Analyst can answer the question ‘what happened?’

 

A Data Scientist on the other hand will go beyond a traditional analyst and build models and algorithms to solve business problems using statistical tools such as Python, R, Spark, Cloud technologies, Tableau etc. The data scientist has an understanding of ‘what happened’ but will typically go a bit further to answer ‘how we can prevent/predict that from happening?’

 

A Data Engineer is the messenger that carries or moves data around. He is responsible for the data ingestion process, building data pipelines to make it flow seamlessly across source, target systems and also responsible for building the CI/CD (continuous integration, continuous development) pipelines.

 

A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organization to perform Data Analysis. They help in selecting the right database, servers, network architecture, GPUs, cores, memory, hard disk etc.

After every classroom session, you will receive assignments through the online Learning Management System. Our LMS is a state-of-the-art system which facilitates learning at your convenience. We do impose a strict condition – you will need need to complete the assignments in order to obtain your data scientist certificate.

Since this course is a blended program, you will be exposed to a total of 80 hours of instructor-led live training. On top of that you will also be given assignments which could have a total duration running into 60-80 hours. In addition to this, you will be working on a live project for a month. All of our assignments are carried out online and the datasets, code, recorded videos are all accessed via our LMS.

We understand that despite our best efforts, sometimes life happens. In such scenarios you can access all of the course videos in the LMS.

Each student is assigned a mentor during the course of this program. If the mentor determines additional support is needed to help the student, we may refer you to another trainer or mentor.

Each student is assigned a mentor during the course of this program. If the mentor determines additional support is needed to help the student, we may refer you to another trainer or mentor.

Jobs in the field of Data Science in San Francisco

Jobs in the Field of Data Science in San Francisco

The job profiles of Data Science include Data Scientist, Senior Data Scientist, Data Analyst, Python Developer, Data Engineer, Data Scientist- Machine Learning, etc.

Salaries in San Francisco for Data Science

Salaries for Data Science Professionals in San Francisco

The average salary for a Data Scientist in San Francisco at early-Career is $94,534, at mid-career, it is $107,651, and for an experienced Data Scientist, the average salary is $120,530.

Data Science course Projects in San Francisco

Data Science Projects in San Francisco

Data Science with AI and Machine Learning projects are being carried out for forecasting climate changes, Breast cancer detection, fraud detection, etc.

Role of Open Source Tools in Data Science in San Francisco

Role of Open Source Tools in Data Science

Python is a very eminent programming language for Data Science. Along with Python, Knowledge of R and R studio statistical tools is a must.

Modes of Training for Data Science with Python in San Francisco

Modes of Training for Data Science

360DigiTMG offers both classrooms as well as online training for students. It also provides individual mentorship to the students.

Industry Application of Data Science in San Francisco

Industry Applications of Data Science

The industrial applications of Data Science are vast and are extensively used in industries that include Automation, Manufacturing, Airlines, Food, Pharmaceutical, Finance, Healthcare, Education, Oil and gas industry, etc.

Companies That Trust Us

360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.

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