• No products in the cart.

SCOPE OF DATA SCIENCE

Data Science is one of the fastest evolving fields & a Data Scientist’s job is one of the fastest growing and highest paid in tech.

As there is a cut-throat competition in the market, top organizations are turning their minds to data analytics to identify new market opportunities to design their services and products. Surveys show that 75% of top organizations consider data analytics an essential component of business performance. This is where data scientists come in. Data scientists know how to use their skills in math, statistics, programming, and other related subjects to organize large data sets. Then, they apply their knowledge to uncover solutions hidden in the data to take on business challenges and goals. They are thus able to contribute to their organization’s business goals. So, learning data science through effective training can give you a bright future.

“Data Scientist” has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! It is a rewarding career that allows you to solve some of the world’s most interesting problems!

As per Payscale.com, a Data Scientist (IT) with Big Data Analytics skills earns an average salary of Rs 706,750 per year in India.

COURSE OBJECTIVE

Skill Venue is delighted to offer classroom program for candidates to help them build a career in the blooming field of Data Science.The program focuses on building foundation skills in business analytics and data science with in depth training of statistical platform & tools.

We will develop candidates to think analytically and solve business problems using data.
Our innovative approach to analytics training by combining deep knowledge and collaborative learning environment will help candidates to develop real skills in analytics.

Candidates taking this course can expect to gain the knowledge of analytical techniques required by organizations to take strategic decisions, and by solving case studies they will know how analytical techniques can be used to get real insights from real data. Candidates will be able to forecast sales, Identify customer segments, identify drivers of sales/profit, perform regression, analyze customer comments and do much more after the course.

In-depth course coverage, hands-on experience of entire data analytics project cycle, and case studies on real world analytics problems, cutting across different domains are the high points that make Skill Venue’s Data Science in R certification course a leap towards a successful analytics career.

 

WHO SHOULD TAKE THIS COURSE

  1. IT professionals looking for career or technology change to  Data Science & Analytics.
  2. BPO industry  or non technical  professionals who are looking for career in Data Science & Analytics.
  3. Engineering graduates who want to build a career in Data Science & Analytics.
  4. Non- technical & MBA graduates who are interested in building a career in Data Science.
  5. Anyone who likes manipulating data & loves getting insights out of data.
  6. Prerequisite – There are not prerequisite for this course except hard work & dedication.

 

 

Course Curriculum

Introduction to the Course
Introduction to Data ANalysis 00:00:00
[Essential Excel Knowledge 1] - Excel Formula Basics & Important Excel Functions
0201 Operators Used in Formulas and How to Build Basic Formulas 00:00:00
0202 Operator Precedence in Formulas 00:00:00
0203 How Built in Excel Functions Make Your Job Easier 00:00:00
0204 Inserting Functions into Formulas Effectively 00:00:00
0205 Relative Cell References – Using Relative Cell References to Copy Formulas 00:00:00
0206 Handling Circular References 00:00:00
0207 Using Named Cells and Ranges in the Formulas 00:00:00
0208 Using Names for Constants & Formulas & Applying Names to Existing Reference 00:00:00
0209 Union and Intersection of Ranges 00:00:00
0210 SUM(…) Function – The Most Useful Function in Excel 00:00:00
0211 Mathematical Functions: Average(), Max() and Min() 00:00:00
0212 Mathematical Functions: INT(), MOD(), ROUND(), RAND(), and RANDBETWEEN() 00:00:00
0213 Count Functions in Excel 00:00:00
0214 Learn About 3-D Reference in Excel 00:00:00
0215 Introducing Array Formulas – How to Enter Arrays into Worksheet Cells 00:00:00
0216 Introducing Array Formulas – How to Create Array Formulas with Examples 00:00:00
0217 INDEX(…) Function in Excel 00:00:00
0218 VLOOKUP(…) Function in Excel 00:00:00
[Essential Excel Knowledge 2] - Excel Tables & Using Structured References
0301 What is a Table? How a Table Differs from a Normal Range 00:00:00
0302 Adding and Deleting Rows or Columns, and Working with the Total Row 00:00:00
0303 Removing Duplicate Rows from a Table or a Range 00:00:00
0304 Sorting and Filtering a Table and Introducing Custom AutoFilter 00:00:00
0305 Using Slicer to Filter a Table 00:00:00
0306 Using Formulas in Tables – Summarizing and Referencing Data in a Table 00:00:00
0307 Use Structured References in Excel Tables 00:00:00
Foundational Concepts of Statistical Data Analysis
0401 – Calculating Mean and Median Values 00:00:00
0402 – Measuring Maximums, Minimums, and Other Data Characteristics 00:00:00
0403 – Analyzing Data Using Variance and Standard Deviation 00:00:00
0404 – Introducing Central Limit Theorem 00:00:00
0405 – Analyzing a Population Using Data Samples 00:00:00
0406 – Identifying and Minimizing Sources of Error 00:00:00
Visualizing Data
0501 – Grouping Data Using Histograms 00:00:00
0502 – Identifying Relationships Using XY Scatter Charts 00:00:00
0503 – Visualizing Data Using Logarithmic Scales 00:00:00
0504 – Adding Trend Lines to Charts 00:00:00
0505 – Forecasting Future Results 00:00:00
0506 – Calculating Running Averages 00:00:00
Testing a Hypothesis
0601 – Formulating a Hypothesis 00:00:00
0602 – Interpreting the Results of Your Analysis 00:00:00
0603 – Considering the Limits of Hypothesis Testing 00:00:00
Utilizing Data Distributions
0701 – Using the Normal Distribution 00:00:00
0702 – Using the Exponential Distribution 00:00:00
0704 – Using the Binomial Distribution 00:00:00
Measuring Co-variance and Correlation
0801 – Visualizing What Co-variance Means 00:00:00
0802 – Calculating Co-variance between Two Columns of Data 00:00:00
0803 – Calculating Co-variance among Multiple Pairs of Columns 00:00:00
0804 – Visualizing What Correlation Means 00:00:00
0805 – Calculating Correlation Between Two Columns of Data 00:00:00
Case Study: Summarizing Data by Using Histograms
0901 – Overview of the section 00:00:00
0902 – Stock Return Analysis Using Histograms 00:00:00
0903 – Common Shapes of Histograms 00:00:00
Case Study: Summarizing Data by Using Descriptive Statistics
1001 – Overview of the section 00:00:00
1002 – How to Get Descriptive Statistics Using Excel’s Data Analysis ToolPak 00:00:00
1003 – What defines a typical value (or centrality) for a data set? 00:00:00
1004 – How can I measure how much a data set spreads from its typical value? 00:00:00
1005 – What do the mean and standard deviation of a data set tell me about? 00:00:00
1006 – How can I use descriptive statistics to compare data sets? 00:00:00
1007 – How to easily find the second largest or second smallest number in a data 00:00:00
1008 – How can I rank numbers in a data set? 00:00:00
1009 – What is the trimmed mean of a data set? 00:00:00
1010 – Alternative of Data Analysis ToolPak 00:00:00
1011 – Why financial analysts use Geometric mean instead of arithmetic average? 00:00:00
Case Study: Estimating Straight-line Relationships
1101 – Overview of the section 00:00:00
1102 – A brief introduction to dependent and independent variable 00:00:00
1103 – The relationship between monthly production & operating costs 00:00:00
1104 – Meaning and significance of R-square value 00:00:00
1105 – Standard error of regression to measure the accuracy of a relationship 00:00:00
1106 – Find intercept, slope and R-square values using Excel’s functions 00:00:00
Case Study: Modeling Exponential Growth
1201 – Overview of the section 00:00:00
1202 – How can I model the growth of a company’s revenue over time? 00:00:00
"Case Study: Using Correlations to Summarize Relationships "
1301 – Overview of the section 00:00:00
1302 – Measuring correlation using Excel’s Data Analysis ToolPak 00:00:00
1303 – Filling the matrix 00:00:00
1304 – Relation between correlation and R-square value 00:00:00
Case Study: Using Moving Averages to Understand Time Series
1401 – Overview of the section 00:00:00
1402 – How to apply the Moving Average based trend line to show a trend 00:00:00
Performing Bayesian Analysis
1501 – Introducing Bayesian Analysis 00:00:00
0 STUDENTS ENROLLED

    Related Courses

    X