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240 Hours

 

Career Path - Data Scientist

Learn Data Science concepts and apply Data Science to practical problems using Python & R programming languages. Become a Data Scientist from scratch.
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Courses included in Data Scientist Career Path Program are:

1) Data Science with Python

2) Data Science with R

3) Python Programming (basic to advanced)

4) R Programming for Beginners

5) R Programming

6) Machine Learning (basic to advanced)

7) Machine Learning with Python

8) Deep Learning Foundation

9) Deep Learning with Keras

10) Deep Learning with TensorFlow

11) Interview Questions - Data Science

12) Interview Questions - Machine Learning

 

Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, scepticism of existing assumptions – to uncover solutions to business challenges. Experienced data scientists and data managers are tasked with developing a company’s best practices, from cleaning to processing and storing data. They work cross-functionally with other teams throughout their organization, such as marketing, customer success, and operations.

Data scientists turn raw data into meaningful information that organisations can use to improve their businesses. Companies are looking for data-driven decision makers. As data scientists, you will extract, analyse and interpret large amounts of data from a range of sources, using algorithmic, data mining, artificial intelligence, machine learning and statistical tools, in order to make it accessible to businesses. Once you've interpreted the data you'll present your results using clear and engaging language.

 

Roles & responsibilities of a Data Scientist

A Data scientist’s responsibilities may include:

1) Extract huge volumes of structured and unstructured data. They query structured data from relational databases using programming languages such as SQL. They gather unstructured data through web-scraping, APIs, and surveys.

2) Perform exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities.

3) Discovering new algorithms to solve problems and build programs to automate repetitive work.

4) Communicate predictions and findings to management and IT departments through effective data visualizations and reports.

 

Data scientists are in high demand across a number of sectors, as businesses require people with the right combination of technical, analytical and communication skills. This Data Scientist Career Path by Uplatz will teach you the skills you need to become just that. You'll learn to analyze data, communicate your findings, and even draw predictions using machine learning. Along the way, you'll build portfolio-worthy projects that will help you get job-ready.

Course/Topic 1 - Data Science with Python - all lectures

  • In this video tutorial we will get introduced to Data Science and the integration of Python in Data Science. Furthermore, we will look into the importance of Data Science and its demand and the application of Data Science.

    • 1:01:14
  • In this video we will learn, all the concepts of Python programming related to Data Science. We will also learn about the Introduction to Python Programing, what is Python Programming and its History, Features and Application of Python along with its setup. Further we will see how to get started with the first python program.

    • 59:19
  • This video talks about the Variable and Data Types in Python Programming. In this session we will learn What is variable, the declaration of variable and variable assignment. Further we will see the data types in python, checking data types and data type conversions.

    • 27:05
  • This tutorial will help you to understand Data Types in python in depth. This video talks about the data types such as numbers, sequence type, Boolean, set and dictionary.

    • 55:27
  • This tutorial talks about the Identifier, keyword, reading input and output formatting in Data Science. We will learn about what is an identifier and keywords. Further we will learn about reading input and taking multiple inputs from a user, Output formatting and Python end parameter.

    • 49:19
  • This tutorial talks about taking multiple inputs from user and output formatting using format method, string method and module operator.

    • 44:09
  • This tutorial talks about the Operators and type of operators. In this session we will learn about the types of operators such as arithmetic, Relational and Assignment Operators.

    • 27:52
  • This tutorial talks further about the part 2 of operators and its types. In this session we will learn about the types of operators such as Logical, Membership, Identity and Bitwise Operators.

    • 31:22
  • In this video you will learn about the process of decision making in Data Science. Furthermore, this tutorial talks about different types of decision-making statements and its application in Data Science.

    • 45:23
  • In this video tutorial we will learn about the Loops in Python programing. We will cover further the different types of Loops in Python, starting with: For Loop.

    • 32:47
  • In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: While loop and nested loop.

    • 39:43
  • In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: break, continue and pass loops

    • 23:13
  • In this video tutorial we will start explaining about the lists in Python Programming. This tutorial talks about accessing values in the list and updating the list in Data Science.

    • 46:54
  • In this video tutorial we will look into the further parts about the lists in Python Programming. Deleting list elements, basic list operations, built in functions and methods and the features which are covered in this session.

    • 40:30
  • This tutorial will cover the basics on Tuples and Dictionary function in Data Science. We will learn about accessing and deleting tuple elements. Further we will also cover the basic tuples operations and the built in tuple functions and its methods. At the end we will see the differences in list and tuple.

    • 53:32
  • This tutorial will cover the advanced topics on Tuples and Dictionary function in Data Science. Further in this session we will learn about the Python Dictionary, how to access, update and delete dictionary elements. Lastly we will cover built in functions and methods.

    • 51:22
  • In this session we will learn about the functions and modules used in Data science. After watching this video, you will be able to understand what is a function, the definition of function and calling a function.

    • 44:01
  • In this session we will learn about the further functions and modules used in Data science. After watching this video, you will be able to understand the ways to write a function, Types of functions, Anonymous Functions and Recursive functions.

    • 43:16
  • In this session we will learn about the advanced functions and modules used in Data science. After watching this video, you will be able to understand what is a module, creating a module, import statement and locating modules.

    • 48:21
  • This tutorial talks about the features of working with files. In this video we will learn about opening and closing file, the open function, the file object Attributes, the close method, reading and writing files.

    • 1:05:09
  • This tutorial talks about the advanced features of working with files. In this video we will learn about file positions, renaming and deleting files.

    • 26:50
  • In this session we will learn about the regular expression. After this video you will be able to understand what is a regular expression, meta characters, match function, search function, Re- match vs research, split function and sub function.

    • 1:02:45
  • This video introduces you to the Data Science Libraries. In this video you will learn about the Data science libraries: libraries for data processing, modelling and data visualization.

    • 45:35
  • In this session we will teach about the components of python ecosystem in Data Science. This video talks about the Components of Python Ecosystem using package Python distribution Anaconda and jupyter notebook.

    • 54:24
  • This tutorial talks about the basics of analyzing data using numpy and pandas. The Pandas module mainly works with the tabular data, whereas the NumPy module works with the numerical data. We will further see what is Numpy and why we use numpy.

    • 56:58
  • This tutorial talks about the later part of analyzing data using numpy and pandas. In this tutorial we will learn how to install numpy.

    • 43:37
  • This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn what is Pandas and the key features of Pandas. We will also learn about the Python Pandas environment setup.

    • 37:21
  • This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn about Pandas data structure with example.

    • 1:12:53
  • This the last session on Analysing Data using Numpy and Pandas. In this session we will learn data analysis using Pandas

    • 28:31
  • In this video tutorial we will learn about the Data Visualization using Matpotlib. This video talks about what is data visualisation, introduction to matplotlib and installation of matplotlib.

    • 37:45
  • In this session we will see the part 2 of Data Visualization with Matplotlib. This video talks about the types of data visualization charts and line chart scatter plot

    • 43:41
  • This tutorial covers part 3 of Data Visualization with Matplotlib. This session covers the types of data visualisation charts: bar chart histogram, area plot pie chart and box plot contour plot.

    • 1:09:26
  • This session talks about the Three-Dimensional Plotting with Matplotlib . In this we will learn about plot 3D scatter, plot 3D contour and plot 3D surface plot.

    • 1:03:43
  • In this tutorial we will cover basics of Data Visualisation with Seaborn. Further we will cover Introduction to seaborn, seaborn functionalities, how to install seaborn and the different categories of plot in seaborn

    • 41:53
  • In this tutorial we will cover the advanced topics of Data Visualisation with Seaborn. In this video we will see about exploring seaborn plots.

    • 59:16
  • Introduction to Statistical Analysis is taught in this video. We will learn what is statistical analysis and introduction to math and statistics for data science. Further we will learn about the terminologies in statistics for data science and categories in statistics, its correlation and lastly mean median and mode quartile.

    • 1:15:05
  • This video course talks about the basics of Data Science methodology. We will learn how to reach from problem to approach.

    • 47:33
  • In this session we will see Data Science Methodology from requirements to collection and from understanding to preparation.

    • 44:12
  • In this session we will learn advanced Data Science Methodology from modelling to evaluation and from deployment to feedback.

    • 39:25
  • This video tutorial talks about the - Introduction to Machine Learning and its Types. In this session we will learn what is machine learning and the need for machine learning. Further we will see the application of machine learning and different types of machine learning. We will also cover topics such as supervised learning, unsupervised learning and reinforcement learning.

    • 56:17
  • This video tutorial talks about the basics of regression analysis. We will cover in this video linear regression and implementing linear regression.

    • 1:11:51
  • This video tutorial talks about the further topics of regression analysis. In this video we will learn about multiple linear regression and implementing multiple linear regression.

    • 54:25
  • This video tutorial talks about the advanced topics of regression analysis. In this video we will learn about polynomial regression and implementing polynomial regression.

    • 38:18
  • In this session we will learn about the classification in Data science. We will see what is classification, classification algorithms and Logistic regression. Also we will learn about implementing Logistic regression.

    • 1:08:43
  • In this session we will learn about the further topics of classification in Data science, such as decision tree and implementing decision tree.

    • 38:36
  • In this session we will learn about the advanced topics of classification in Data science, such as support vendor machine and implementing support vector machine.

    • 25:37
  • This tutorial will teach you about what is clustering and clustering algorithms. Further we will learn what K means clustering and how does K means clustering work and also about implementing K means clustering.

    • 53:10
  • In this session we will see the further topics of clustering, such as hierarchical clustering, agglomerative hierarchical clustering, how does agglomerative hierarchical clustering Work and divisive hierarchical clustering.

    • 28:55
  • This video tutorial talks about the advanced topics of clustering, such as implementation of agglomerative hierarchical clustering.

    • 33:58
  • This video will help you to understand basics of Association rule learning. In this session we will learn about the Apriori algorithm and the working of Apriori algorithm.

    • 53:30
  • This video will help you to understand advanced topics of Association rule learning such as implementation of Apriori algorithm.

    • 58:45
  • This is a session on the practical part of Data Science application. In this example we will see problem statement, data set, exploratory data analysis.

    • 56:40
  • This is a session on the practical part of Data Science application.

    • 42:39
  • This is a session on the practical part of Data Science application. In this we will see the implementation of the project.

    • 50:54
  • This is a session on the practical part of Data Science application

    • 38:18
  • This is a session on the practical part of Data Science application

    • 1:02:31

Course/Topic 2 - Data Science with R - all lectures

  • In this lecture session we learn about introduction of data science and also talk about features of data science in R.

    • 54:03
  • In this lecture session we learn about data collection and management and also talk about features of data collection and management in data science with R.

    • 29:32
  • In this lecture session we learn about model deployment and maintenance and also talk about functions of model deployment and maintenance in data science with R.

    • 12:47
  • In this lecture session we learn about setting expectations and also talk about factors of setting expectations in brief.

    • 10:18
  • In this lecture session we learn about loading data into R and also talk about features of loading data into R and also talk about the importance of loading data into R.

    • 42:20
  • In this lecture session we learn about exploring data in data science and machine learning and also talk about features of exploring data in data science and machine learning.

    • 08:01
  • In this lecture session we learn about features of exploring data using R and also talk about factors of exploring data using R.

    • 45:36
  • In this lecture session we learn about benefits of data cleaning and also talk about features of benefits of data cleaning.

    • 22:44
  • In this lecture session we learn about cross validation in R and also talk about features of validation in data science with R.

    • 17:32
  • In these lecture sessions we learn about data transformation in data science with R and also talk about features of data transformation in brief.

    • 1:35:26
  • In this lecture session we learn about modeling methods in data science with R and also talk about the importance of modeling methods.

    • 20:13
  • In this lecture session we learn about solving classification problems and also talk about features of solving classification problems in brief.

    • 11:55
  • In this lecture session we learn about working without known targets in data science with r and also talk about features of working without known targets.

    • 19:58
  • In this lecture session we learn about evaluating models in data science with R and also talk about features of evaluating models in brief.

    • 28:11
  • In this lecture session we learn about confusion matrix in indian accounting standards and also talk about features of confusion matrix.

    • 34:03
  • In this lecture session we learn about introduction to linear regression and also talk about features of linear regression in indian accounting standards.

    • 1:25:24
  • In this lecture session we learn about linear regression in R and also talk about features and functions of linear regression in brief.

    • 26:51
  • In this lecture session we learn about linear regression in R in data science with r and also talk about features of linear regression in R language.

    • 41:22
  • In this lecture session we learn about simple and multiple regression in data science with r and also talk about the basic difference between simple and multiple regression in brief.

    • 56:54
  • In this lecture session we learn about linear and logistic regression in data science with r language and also talk about functions of linear and logistics regressions.

    • 29:09
  • In this lecture session we learn about support vector machines (SVM) in R and also talk about features of support vector machines in data science with R language.

    • 45:18
  • In this lecture session we learn about factors of support vectors machines in data science with R and also talk about features of support vectors machines.

    • 1:30:55
  • In this lecture session we learn about unsupervised methods in data science with R and also talk about functions of unsupervised methods in data science.

    • 24:44
  • In this lecture session we learn about clustering in data science with R language and also talk about features of clustering in data science.

    • 50:44
  • In this lecture session we learn about K-means algorithms in R and also talk about all types of algorithms in data science with R language.

    • 1:09:44
  • In this lecture session we learn about hierarchical clustering in data science with R language and also talk about features of hierarchical clustering.

    • 33:41
  • In this lecture session we learn about libraries in data science with R and also talk about libraries of hierarchical clustering in brief.

    • 23:15
  • In this lecture session we learn about the dendrogram of diana and also talk about all types of clustering in data science with R.

    • 41:05
  • In this lecture session we learn about market basket analysis in data science with R and also talk about features of market basket analysis in data science with R.

    • 05:08